Face Recognition


On the BBC Newsnight Report on Terrorism and Face Recognition   Recently updated !

Richard Watson’s report on the 20th July edition of BBC Newsnight on the threat of terrorism and and the potential use of face recognition to combat it was informative, balanced and effectively presented the scale of the challenges faced, as well as the challenges in any potential use of the technology.

It’s important to realise that face recognition is not a panacea. But it is an effective tool that can drastically improve the efficiency of our intelligence and police agencies.

 

The figure cited of 40 officers to trail one suspect full time over 20 hours is not off the mark. Ex Chief Constable of the British Transport Police Andrew Trotter neatly summed it when he said “It is a huge labour intensive. Huge, and these people may do nothing for months, years. … And all the time there might be others that needed more attention. That diverts resources from other things…”.

Products such as the one demonstrated by Zak Doffman of Digital Barriers present an excellent example of technology available and typical and effective uses of them. However, Roger Cumming hit the nail on the head when he said “If an alarm is rung through your camera system picking up one of these people, what do you actually do? Because at that state all you’ve got is a positive identification of somebody on a watchlist? Do they represent a threat? Are the planning some sort of attack? Or are they just going about their business?…”.

The use of the technology in itself will pose new challenges.

The demonstrated solution is just one of many that can effectively perform live watchlist alerting on surveillance cameras to positive effect. But I don’t believe this use of face recognition alone represents the greatest benefit to police, and may not sufficiently improve efficiency to warrant the cost and generate a return on the investment.

It is likely that after each of the terrorists attacks in recent months, there was a substantial quantity of video evidence that needed to be manually reviewed at great use of resource. A significant challenge faced by police is the effective and efficient extraction and linking of intelligence from all of this media which can come from multiple sources, including CCTV, body worn video, members of the public, the Internet, the dark Web, news broadcasts and digital forensics (confiscated computers, phones, drives etc).

This represents a phenomenal Big Data challenge which is becoming increasingly solvable with the increasing availability of on-demand and elastic cloud computing paradigms. If we can rapidly process this media (at much greater speed than real time) and extract assets using face recognition and other detection technologies for presentation to reviewing investigators, this will significantly reduce the amount of time they need to laboriously watch it themselves, whilst increasing the intelligence they obtain from it, enabling them to rapidly “connect the dots”.

More actionable and linked intelligence, obtained quicker and cheaper. This can feed the watchlists the live surveillance cameras are searching in an integrated fashion off the same platform.

And perhaps just as importantly, introduce efficiency to feed a business case to help ensure the technology can be feasibly adopted.

You can watch the programme here: http://www.bbc.co.uk/programmes/b006mk25


CyberExtruder Announces the Release of Aureus 3D Version 5

Allevate is pleased to be cooperating with CyberExtruder who have just announced the release of Aureus 3D Version 5.7, which achieves product performance at speeds unprecedented in the industry.

CyberExtruder says “With our 128-byte face template size – the smallest in the industry – we can perform matching on a database of 7.5 billion people (the world population) in 4.69 seconds. With speeds at this level coupled with superior accuracy and scalability, Aureus 3D Version 5.7 will change the industry. There’s nothing of its kind anywhere.”

 


Allevate Quoted in a BBC News Article on Face Recognition

Allevate’s founder is quoted in an article by Mark Smith, entitled “Smile, you’re on camera, and it knows who you are“.

Carl Gohringer, founder and director at Allevate, a facial recognition firm that works with law enforcement, intelligence and government agencies, says: “The amount of media – such as videos and photos – available to us as individuals, organisations and businesses, and to intelligence and law enforcement agencies, is staggering.

“We’re well beyond the point where all of it is usable or viewable by us as human beings. So technology will be applied that results in new and interesting mechanisms of accessing, analysing, ordering, structuring and processing this visual minefield.”


Happy New Year. A Brief Update from Allevate. See us at Intersec in Dubai

2017 is now well underway so I wanted to take a moment and wish everybody a Happy New Year and provide a brief update.

Face-Searcher launched with Facewatch in Brazil

After having jointly launched Allevate’s new Face-Searcher Face Recognition  as a Service in Brazil last year in partnership with Facewatch, we are very pleased and excited by the uptake of the service that we have been seeing so far.

Upcoming Face-Searcher launch in the UK

Having initially launched our service successfully overseas, we are now working very hard to negotiate hosting agreements with a strategic hosting partner and we will be looking to launch the Face-Searcher service, integrated with Facewatch, in the UK in the near future as a SaaS cloud-service to businesses and organisations.

We already have some notable organisations scheduled to trial our service in the coming weeks who who are looking to enhance the security and improve the safety of crowded places they manage.

If your organisation would like a very easy-to-setup trial, please do not hesitate to contact us.

Seeking International Distributors and Partners

We are strategically seeking to roll-out our integrated SaaS cloud-hosted online crime reporting and face recognition service globally in targeted countries.

We are actively seek organisations to partner with us to enable them to offer our service within their countries. Please contact us to find out more.

Successful Participation Securing Crowded Places Immersive Demonstrator at UK Security Expo

We were very pleased to have participated in the Home Office’s Crowded Places Demonstrator at UK Security Expo at the end of last year. Thank you to very much to our partners Facewatch and Physical Tracking Systems for their support and an extra large thank you to Sungard Availability Systems for their full support in enabling our participation.

See you at Intersec Dubai Next Week, 22-24 January

Allevate is very pleased to be attending Intersec, the world’s leading trade fair for Security, Safety and Fire Protection, in Dubai next week where we have a multiple meetings scheduled with potential clients and partners throughout the Middle East Region.

If you are interested in meeting with us next week in Dubai to discuss how you may benefit from the use of Allevate’s offerings, or to discuss possible partnership and potential to collaborate in the region, please do not hesitate to contact us.

Thank you once again and I look forward to speaking with many of you in 2017.


The Rise of Anti-Surveillance Clothing

There has been increasing press coverage pertaining to developments of anti-surveillance clothing and paraphernalia to counter the effectiveness of face recognition, such as this recent article in the Guardian: Anti-surveillance clothing aims to hide wearers from facial recognition.

 

 

The real issue with regards to the continuous development of anti-surveillance paraphernalia and the ability of technology suppliers to circumvent it is not an issue of technology, but rather a social one. Advocates and opponents will continuously be leap-frogging each other with their ability to detect and to counter.

What we should be focusing on is understanding the reason for dissent and working together as a society to develop an ethical and moral code of conduct. Innocent people rightly have an expectation of privacy and do not want to be followed, tracked or traced. We’re often asked “Why do you care what I’m doing? Or where I’m going? Or what I’m doing?” And the answer, simply, is “We don’t.”

At Allevate, together with our partners Facewatch, our goal, our self-directed mandate, is to improve and better society. To create safe places for people to gather, to minimise the threat of crime and attack and to aid the authorities in identifying and apprehending those that seek to do the opposite.

However, like many technologies, there is the potential for face recognition technology to serve multiple purposes. In our experience, society does not object to safe-guarding our children, reducing crime and the threat of terrorist attack and making our world a safer place. The objections arise when it is the law-abiding citizen being identified for the commercial gain of somebody else, without their consent.

Yes, we will continue to develop mechanisms to ensure we accurately identify people, but the real solution is dialogue. Open and honest. If all non-security applications of such technology are transparent and driven by opt-in and consent, then perhaps the only people that will be trying to reduce its effectiveness are the criminals, which will only serve to make them stand out even more.

You can read more on Allevate’s views on this subject in this whitepaper: Face Recognition: Profit, Ethics and Privacy.


Combatting Crime and Protecting Crowded Places. Face Recognition in the Cloud Demonstrated at UK Security Expo 2016 in London.

 

newsreleaseAllevate’s Cloud-hosted Face-Searcher face recognition service integrated with Facewatch’s digital crime reporting system to feature in UK Security Expo’s Securing Crowded Places Immersive Demonstrator. Nationally available in Brazil, UK launch imminent.

 

LONDON, UK 25th November 2016: Allevate and Facewatch today announce that Allevate’s Face-Searcher, a cloud-hosted face recognition service integrated with Facewatch’s online crime reporting system, will feature in the Securing Crowed Places Immersive Demonstrator at the UK Security Expo on the 30th November and 1st December 2016 at Olympia, London. After its successful Brazilian launch, the integrated offering is now due for imminent launch in the UK.

The Immersive Demonstrator at UK Security Expo 2016 will be under the theme of ‘Securing Crowded Places’ and is being run in association with The Home Office JSaRC, the Centre for the Protection of National Infrastructure (CPNI) and other relevant Government Departments.

Allevate’s Face-Searcher service enables organisations to utilise facial recognition as a hosted cloud service. It requires minimal capital outlay, incorporates advanced, world-class face recognition technology and eliminates the need to install or maintain a complicated software infrastructure or related compute platform on clients’ premises.

Facewatch enables organisations to report crimes online and submit moving and still CCTV images as evidence to the police, as well as share this imagery between businesses in related subscribed groups (in compliance with Data Protection guidelines) to reduce crime.

Following an imminent UK launch, UK Facewatch subscribers will be able to instantly and automatically share their images of Subjects of Interest to Face-Searcher’s watchlists, thereby allowing real-time watchlist alerting to any device connected to Facewatch’s integrated alert management system. This integrated offering will help businesses prevent crime by warning them if someone entering their premises is on a watchlist of known offenders.

Face-Searcher is built on the industry-proven enterprise-grade MXSERVERTM platform enabling automated facial detection and recognition, developed by Tygart Technology, Inc.

Additionally, Allevate will be providing a live demonstration of the MXSERVERTM platform in the Technology Workshops and Live Demonstrations in the conference stream of the exhibition, entitled “Beyond Live Surveillance: The Application of Face Recognition to Improve Forensic Analysis of Masses of Digital Media“, Day 2, 1st December, 2016 at 1240pm.

MXSERVERTM is also available on the UK’s Crown Commercial Service Digital Marketplace G-Cloud 8 Framework and is Powered by Sungard Availability Services.

Find out more on stand A41 at the exposition, in collaboration with Sungard Availability Services.


About Allevate Limited

Founded in London in 2007, Allevate works with law enforcement, intelligence and government agencies to enhance public safety by ensuring positive identification through the application of biometric and identification technology.

  • Ensure Positive Identification
  • Enhance Public Safety
  • Reduce Operational Costs

Visit us at http://allevate.com, email us at contact@allevate.com, call us on +44 20 3239 6399 and follow us at @Allevate.

About Facewatch

Founded in London in 2010, Facewatch has worked with UK policing to create the world’s first private sector crime reporting platform that enables business and police to share information securely and instantly.

Visit us at http://www.facewatch.co.uk, email us at info@facewatch.co.uk, call us on +44 20 7930 3225 and follow us at @Facewatch.

About Tygart Technology, Inc.

Tygart Technology, Inc. is a leading provider of enterprise-grade video and photographic analysis and biometric recognition systems. Tygart provides the U.S. Military, Intelligence Community and Law Enforcement markets with innovative software solutions that manage and automate the processing of massive volumes of digital video and photograph collections.

Visit us at http://www.tygart.com or call 1-304-363-6855.


Allevate Seeking Global Distributors and Agents for Face-Searcher Service

Allevate is actively seeking global distributors and agents for its new cloud-hosted Face-Searcher Service integrated with Facewatch’s secure online crime reporting service. The integrated offering enables businesses, public and police to tackle low-level crime by sharing images within groups and then utilise cloud-hosted face recognition to raise alerts back to the business.

Key Benefits:

  • No complicated software to install or maintain.
  • Enables businesses to collaborate with each other and the police by sharing imagery.
  • Integrated with the industry’s best biometric algorithms.
  • Affordable monthly service fee with no or minimal up-front capital expenditure.

 

Already launched in Brazil, the integrated service is available globally and we are now seeking both:

  • Leading security and surveillance organisations to act as a distribution channel.
  • Agents to manage and on-board distributors within specific geographic territories.

The Value to You

Our integrated offering supplies our distribution channel with an affordable, SaaS structured offering to augment your already successful and credible security and surveillance operation with a cloud-hosted and easy to maintain facial recognition service.

Coupled with your extensive experience in CCTV installation and configuration and control room and monitoring services, it adds further value that enables your customers to collaborate with each other and the police and to further enhance the security of their premises with face recognition.

 

If you are interested or to find out more, please do not hesitate to contact us here.

Download a datasheet on Face-Searcher here.

 

 

 


Allevate’s Face-Searcher in Action: Cloud-Enabled Face Recognition

A test-run of Allevate’s Face-Searcher service integrated with the online Facewatch crime reporting system:

  • A camera with a lightweight laptop in the UK
    • Detecting and cropping faces from the video stream
  • Submitting image files of cropped faces for ultra-scalable and accurate face matching in the Cloud in Amazon Web Services in USA.

… with

  • Watchlist data syncronised with a Facewatch test instance in Amazon Web Services in Brazil

… reporting

  • Alerts to the Facewatch test subscriber back in the UK, all in under 3 seconds from sighting of suspect.

Why?

  • Because we can, and to demonstrate the flexibility of our cloud-based matching system.

 

Available now in Brazil., hosted locally in Brazil.

Coming soon elsewhere.

 

Download the Face-Searcher datasheet here.

 

Face-Searcher with Facewatch


Allevate Cooperates with Civica UK Ltd to Help Enable their Digital Biometric Forensic Services Proposition on GCloud

 

Allevate is pleased to be co-operating with Civica UK Ltd.to help enable their service offering on the UK’s Crown Commercial Service Digital Marketplace G-Cloud 8.



Digital Biometric Forensic Services

A holistic service for the management and processing of Forensic Evidence and Records. Providing a central asset management hub and additional best of breed modules for fingerprint, photo and video analysis and face recognition software. Proven integration with third party applications and hosted in a compliant IL3 environment.

Features

  • Civica FileTrail Evidence and Asset Tracking software
  • FISH Forensic Image Scanning Hub
  • MXServer Face Recognition Software
  • Sungard IL3 Compliant Official Assured Zone Managed Cloud Services
  • Full end to end Digital Biometric Forensics Management
  • Complete Audit Trail and Reporting
  • Fully configurable Workflow Dashboards
  • Seamless integration
  • Best of breed applications and hosting
  • Single modular solution

Benefits

  • Fully configurable to meet a wide range of requirements
  • Significant reduction in staff inputting and processing time
  • Demonstrable return on investment
  • Automated barcode and RFID tracking
  • Simple and intuitive interface
  • Faster detection of offenders
  • Extends an organisation’s intelligence base
  • Secure hosting with full Disaster Recovery
  • Relives the strain on existing IT infrastructure
  • Proven track record in this field

Cloud Face Recognition Integrated with Secure Online Crime Reporting Launched in Brasil

 

newsreleaseAllevate’s Cloud-Hosted Face-Searcher Face Recognition Service integrated with the Facewatch secure online crime reporting system is launched in Brasil. This integrated offering, to all organisations large or small, enables the provision of face recognition in the cloud, matching against data-sets created from real-time crime reporting.  

LONDON, UK and Rio de Janeiro, Brasil 17 August 2016:  Allevate today announces the launch of its Face-Searcher service, enabling organisations, large or small, to utilise facial recognition as a hosted cloud service. Additionally, Facewatch, the secure online crime reporting system, announces immediate availability of an integrated Facewatch and Face-Searcher offering, launching initially in Brasil.

Facewatch enables organisations to report crimes online and submit moving and still CCTV images as evidence to the police, as well as share this imagery between businesses in related subscribed groups (in compliance with Data Protection guidelines) to reduce crime.

Allevate’s Face-Searcher service enables organisations to utilise facial recognition as a hosted cloud service. It requires minimal capital outlay, incorporates advanced, world-class face recognition technology and eliminates the need to install or maintain a complicated software infrastructure or related compute platform on your premises. The Face-Searcher Edge component detects and crops faces from retailyour CCTV cameras and submits them to the cloud-service for matching.

Facewatch subscribers can now instantly and automatically share their images of Subjects of Interest to Face-Searcher’s watchlists, thereby allowing real-time watchlist alerting to any device connected to Facewatch’s integrated alert management system. This integrated offering can now help businesses prevent crime by warning them if someone who enters their premises is on a watchlist of known offenders.

Simon Gordon, of Facewatch, says “A major factor that has hindered the wide-scale adoption of face recognition by business has been the requirement to install and manage costly and complex software. Allevate’s cloud-hosted SaaS offering removes this headache and enables businesses to benefit from the accuracy of the industry’s best face recognition algorithms in a cloud-enabled shared-services environment with a simple easy to understand monthly subscription fee.”

Carl Gohringer, of Allevate, continues “The best face-recognition technology in the world is useless unless businesses have accurate and reliable subject matter to match against. Facewatch has already proven invaluable in enabling businesses to seamlessly interact with their local police. Now, they can co-operate by sharing this same imagery amongst their local business groups.”

Face-Searcher is built on the industry-proven enterprise-grade MXSERVERTM platform enabling automated facial detection and recognition, developed by Tygart Technology, Inc. MXSERVERTM is the only biometric search engine on the market designed to handle Big Data (processing massive amounts of photos and videos) by leveraging a cloud-based architecture for faster parallel processing of services.  MXSERVERTM is proven and utilised by Defence, Intelligence and Law Enforcement organisations and has also been used to enhance security at major events, such as the 2015 European Games, a major international sporting event.

This integrated offering is being made available in Brasil by our local partner, Staff Security Ltd. Humberto Bambira, of Staff Security, says “We have been developing this exciting opportunity for the mass market rollout of facial recognition in Brasil with Facewatch for two years and I am delighted to see the launch of the service.”


About Allevate Limited

Founded in London in 2007, Allevate works with law enforcement, intelligence and government agencies to enhance public safety by ensuring positive identification through the application of biometric and identification technology.

  • Ensure Positive Identification
  • Enhance Public Safety
  • Reduce Operational Costs

Visit us at http://allevate.com , email us at contact@allevate.com, call us on +44 20 3239 6399 and follow us at @Allevate.

 

About Facewatch

Founded in London in 2010, Facewatch has worked with UK policing to create the world’s first private sector crime reporting platform that enables business and police to share information securely and instantly.

Visit us at http://www.facewatch.co.uk,  email us at info@facewatch.co.uk, call us on +44 20 7930 3225 and follow us at @Facewatch.

 

About Tygart Technology, Inc.

Tygart Technology, Inc. is a leading provider of enterprise-grade video and photographic analysis and biometric recognition systems.  Tygart provides the U.S. Military, Intelligence Community and Law Enforcement markets with innovative software solutions that manage and automate the processing of massive volumes of digital video and photograph collections.

Visit us at www.tygart.com or call 1-304-363-6855.

 


Allevate Announces Availability of SaaS Face Recognition Service on UK’s Digital Marketplace (G-Cloud 8)

newsreleasePowerful Cloud-Enabled Video and Photographic Forensic Analysis System Incorporating Face Recognition is available to all UK government, intelligence and law-enforcement agencies to assist in combatting crime and terrorist activities. MXSERVER, enabled by Sungard Availability Services, automates the bulk-processing of media for forensic analysis and is already proven by US Federal agencies to provide an “Order of Magnitude” efficiency gain and significantly enhanced identification of suspects.

LONDON, UK 02 August 2016:  Allevate today announces that MXSERVER is available on the UK Crown Commercial Service’s Digital Marketplace G-Cloud 8 Framework.  Allevate’s SaaS G-Cloud offering is enabled by Sungard Availability Services, who provides a Secure Managed Cloud IaaS and PaaS platform, with OFFICIAL classification, for UK Government Service Provision.

Our security services are faced with a relentless increase in digital media — from CCTV and surveillance cameras, police body worn video, online sources such as Facebook and YouTube, confiscated phones and computers and, increasingly, ‘crowd-sourced’ from members of the public. There has been no easy and cost-effective way to access the intelligence this media contains. Experienced and expensive human capital has been assigned the rote task of watching countless hours of video in the hope of finding useful information.

MXSERVER, from Tygart Technology, processes vast amounts of textual, video and photo collections quickly – automatically discovering, grouping and extracting segments depicting people. Using face recognition technology, this solution searches media archives to find other assets which depict individuals of interest. It also indexes the digital media to enable it to be efficiently searched using a photograph of a face, previewed and analysed via an intuitive web-based user interface. Results become available in minutes rather than hours or days because the digital media files are processed in parallel over a distributed cloud-architecture.

Allevate emphasizes that “MXSERVER delivers a Big Data solution for law-enforcement’s growing video and photo assets. It provides a significantly enhanced identification capability that is quicker and more efficient than manually watching video. “

From today, access to both the software and all hosting and storage services are available on Digital Services Marketplace G-Cloud 8 framework using an easy to calculate monthly service fee. The G-Cloud catalogue is open to all public sector clients and is designed to provide a simple streamlined process for buying ICT focused products and services as a commodity without having to invite tenders from suppliers.

About Allevate Limited

Founded in London in 2007, Allevate works with law enforcement, intelligence and government agencies to enhance public safety by ensuring positive identification through the application of biometric and identification technology.

  • Ensure Positive IdentificationCrown-Commercial-Service-Supplier_logo
  • Enhance Public Safety
  • Reduce Operational Costs

Visit us at http://allevate.com , email us at contact@allevate.com, call us on +44 20 3239 6399 and follow us at @Allevate.

About the UK Crown Commercial Service

The UK Crown Commercial Service (CCS) works with both departments and organisations across the whole of the public sector to ensure maximum value is extracted from every commercial relationship and improve the quality of service delivery. The CCS goal is to become the “go-to” place for expert commercial and procurement services.


“Facial recognition system was used on live video from surveillance cameras at the 2015 European Games, in Baku, Azerbaijan”

From nextgov.com US SPIES TRAIN COMPUTERS TO SPOT SUSPICIOUS ACTIVITY IN LIVE VIDEOS

“Last year, a facial recognition system was used on live video from surveillance cameras at the European Games, in Baku, Azerbaijan, according to the tool’s developer, Tygart. During the June 2015 event, organizers watched a webpage that could issue an alert if a face in the crowd matched that of an individual on a watch list. ”

Baku2015 was the inaugural European Olympic Committee’s First European Games and was attended by 6,000 athletes from over 50 countries over 16 days, with over 600,000 tickets sold.

MXSERVER is a highly scalable cloud-enabled solution to process videos and photographs applying face recognition.

All faces in the media are:

  • Extracted and cropped
  • Searched against a watchlist
  • Indexed so the media can be searched with a photograph

Media inputs to MXSERVER include:

  • Body Worn Video
  • Live surveillance cameras (CCTV)
  • Archived video
  • Online Sources (YouTube, Facebook etc)
  • Confiscated hardrives, phones, PCs (Digital Forensics)

You can view more info on MXSERVER at:
http://allevate.com/index.php/mxserver/mxserver_presentation/

… and read more about the proposition at:
http://allevate.com/index.php/2013/08/01/intelligence-and-efficiency-through-on-demand-media-analysis-using-face-recognition/


Allevate Now Offering Toshiba’s Face Recognition Integrated with the MXSERVER Cloud-Enabled Media Analysis Platform

newsrelease

Integrated Offering Combines MXSERVER’s Proven Ability to Massively Scale the Processing of Vast Quantities of Video and Photographs with the NIST Demonstrated Accuracy of Toshiba’s Face Recognition Library

 

 

 

London, UK — 15 March 2016Allevate Limited today announced that, working cooperatively with Tygart Technology, it is now offering Toshiba’s Face Recognition Software Library as an integrated component of Tygart’s MXSERVER™ to enable European government, law enforcement and security agencies to further enhance public safety. MXSERVER is an algorithm-agnostic, cloud-enabled system that processes vast quantities of video and photo collections to transform these digital assets into searchable resources by using face recognition.

According to Allevate, one of the key strengths of Tygart’s MXSERVER is the fact that it is agnostic to and can be deployed with multiple commercially available face recognition algorithms (COTS) or government developed face recognition algorithms (GOTS). This enables Allevate to work co-operatively with the end-user and algorithm vendors to determine the most appropriate selection of algorithm to meet each client’s unique needs. Additionally, clients have the flexibility to continually improve performance by using the best available algorithm over the life of the project as requirements change. Traditionally, having purchased an entire turn-key platform from a specific face recognition algorithm vendor, clients would have to sacrifice their entire investment in that vendor’s platform should they wish to change the underlying algorithms for any reason. MXSERVER enables clients to leverage their investment in a scalable Enterprise Grade technology platform by only changing the underlying algorithm components.

Toshiba’s Face Recognition Software Library is a Software Development Kit (SDK) that provides automated face detection and tracking in videos and photos, face recognition matching and photograph quality assessment.  The combination of this SDK with MXSERVER will provide government, law enforcement and security agencies with enhanced surveillance, monitoring and forensic analysis capabilities.

An Allevate spokesman said “Toshiba is one of the leading providers of face recognition technology and continues to be one of the top performers as demonstrated by independent testing by the US National Institute of Standards and Technology (NIST)”. He continued “We are very pleased to offer our clients further flexibility with the provision of Tygart’s MXSERVER with a Toshiba-inside option.”

“We are very proud of the accuracy and price performance ratio of Toshiba’s enterprise-grade software algorithms, based on the result of FRV2013 by NIST,” said Nobuyoshi Enomoto, Deputy Senior Manager of Toshiba. “Integration with the MXSERVER cloud-enabled platform strengthens our offering with a scalable search and index capability to support real-time surveillance and monitoring for public security and post-event forensic analysis.” He continues “We are pleased to be working with Allevate to make this joint offering available to Europe’s law-enforcement, intelligence and security agencies.”

—ENDS—

About MXSERVER

MXSERVER is a cloud-architected face recognition system that processes vast quantities of video and photo collections extracted from police body cameras, online sources, surveillance systems, digital forensics and, increasingly, “crowd-sourced” from the public.

MXSERVER can transform these digital assets into searchable resources. Using face recognition technology it searches media archives to find individuals of interest. It also indexes the media to enable it to be searched using a photograph. Trained investigators are freed to intelligently apply their skills without having to view countless hours of media.

 About Allevate Limited

Visit us at http://allevate.com, email us at contact@allevate.com, call us on +44 20 3239 6399.

About Toshiba Corporation

For more information, visit http://www.toshiba.co.jp/sis/en/scd/face/face.htm


Face Recognition in Airports

The accuracy of face recognition has increased dramatically. It is now capable of providing reliable results in real-world environments; the technology is being deployed in airports today to enable everything from automated immigration processes, improved surveillance, security and seamless passenger travel, to the gathering of valuable statistical information pertaining to passenger movements.

1.0 The Business Environment

Airports are complex environments involving multiple stakeholders with conflicting requirements:

  • Government and border control.
  • Police.
  • Airport operators.
  • Airlines.
  • Retailers.

All parties must comply with all Government regulations and utilise the latest documents and passports from multiple issuing states while adhering to all security requirements.

2.0 Current Applications of Face Recognition in Airports

Face recognition has evolved significantly over the past decade and has now attained a level of accuracy that provides real and quantifiable business benefit to all stakeholders in an airport environment. Solutions incorporating face recognition are already being deployed today.

2.1 Automated Border Control Gates at Immigration

Many nations world-wide have deployed e-Passports which are being carried by an ever-increasing percentage of the world’s population. This enables governments to deploy Automated Border Control (ABC) gates. In EU nations for example, these gates:

  • are for EU passport holders.air travel
  • do not require pre-enrolment.
  • perform a 1:1 face verification of a live scan against the JPG on the passport chip.

In the UK these gates are being widely deployed at entry ports and seemingly form the backbone of the government’s strategy for automatically clearing EU passengers.

Many EU member starts are increasingly enabling the use of the gates by non-EU nationals who pre-register in Trusted Traveller systems.

In Asia ABC deployments  process hundreds of thousands of passengers daily, maximising the efficiency of live border guards.

2.1.1 How it Works

The process involved in an ABC gate is fairly simple:

  1. The passenger approaches the gate and has their passport read by the e-gate.
  2. The validity of the data page on the passport is verified using a variety of tests.
  3. The information in the machine readable zone (MRZ) is verified against the data read off the chip.
  4. The passport information is sent to the appropriate government systems for the appropriate checks.
  5. If there are any problems thus far, the passenger is re-directed to a manned border lane, otherwise …
  6. A live photo is captured of the passenger (with appropriate liveness checks).
  7. Face recognition is used to verify the live capture with the photograph read off the passport’s chip.
  8. If the photo does not match, the passenger is assisted by a live border guard, otherwise…
  9. The passenger is allowed to proceed.

In this use of face recognition:

  • FAR represents the percentage of passengers holding a passport that does not belong to them that are wrongly admitted.
  • FRR represents the percentage of legitimate passengers who are wrongly re-directed to a live border guard due to the photographs not matching.

There have been no published studies of the FAR and FRR achieved by a live border guard, but it is generally accepted that face recognition operates at a higher level of accuracy, especially when a border guard has been on operational duty for more than 2 hours or has to deal with visual verification of multiple races of passengers. Most e-gate deployments in Europe today operate with an FRR of approximately 6% set against a corresponding FAR of 0.1%.

Recently, an officer responsible for a large deployment of e-gates in an international airport indicated that in his view, most imposters attempting fraudulent entry into the country prefer to try their luck with manned border guards rather than use automated gates.

2.1.2 The Business Benefit

You don’t have to look far today to read of the burgeoning deficits of most western nations. Austerity is the order of the day. Even in light of the expected year-on-year growth in passenger numbers, budgets are being cut. More and more often, improved efficiencies introduced by the sensible deployment of technology are being relied on to address these budget shortfalls.

Border guards are highly skilled and experienced staff deployed at the front-line of our nations defences. 99% of travellers entering a country are benign. Routine checking of travel documents and verification of valid ownership are tasks that can now be better performed by technology, thereby enabling the automated egress of legitimate travellers and allowing the border guards to focus on and find the 1% of the travellers they really want to speak with. In effect, removing the haystack to reveal the needle.

It is also relevant to note that the higher the accuracy of the face recognition solution deployed, the lower the FRR realised, thereby resulting in fewer passengers redirected to a live border guard and a lower cost of total ownership.

2.1.3 An Example

Another nation that has recently trialled the deployment of 4 ABC lanes determined the following:

  • Without the ABC lanes, 8 manual lanes required 8 border guards.
  • With the ABC lanes, the same 8 border guards were able to monitor 12 lanes.
  • Without the 4 ABC lanes, 8 border guards oversaw the entry of 950 passengers per hour.
  • With the 4 ABC lanes, 8 border guards oversaw the entry of 2,400 passengers per hour.

Even with the deployment of a limited number of ABC lanes a real and tangible benefit was realised.

2.2 Trusted Traveller Systems

Most ABC solutions deployed today take one of two forms:

  • Non-Registered, for holders of e-Passports from authorised countries (as discussed above).
  • Registered, for holders of passports from countries not authorised to use the Non-Registered lanes (or holders of older passports without a chip).

Examples of the latter include the US Global Entry, Dutch Privium (collectively FLUX) and the now retired UK IRIS systems.

As non-registered systems become more commonplace and the number of e-passport holders continues to rise, the business case for governments to provide separate free-to-use Trusted Traveller systems becomes vague. Ideally, given the limited space available in airports, the best scenario involves these passengers using the same physical e-gates as users of the non-registered systems.

Existing e-gates are being modified to accommodate holders of e-Passports from other nations. An additional step in the process flow allows the e-gate to cross-reference against a database of pre-enrolled and vetted Trusted Travellers. An additional face verification can be performed against the stored face details of the enrolled passenger.

2.3 Departure and Boarding Gates

The previous example depicts the use of biometrics to facilitate passenger processing at immigration and to introduce efficiencies to the tasks of border control officials. Airport operators and airlines are also increasingly turning to biometrics to facilitate the flow of outbound passengers through airport terminals.

Simplifying Passenger Travel (SPT) was an initiative led by airlines, airports, governments and technology providers which proposed the “Ideal Process Flow”. The goal was to combine e-passports, biometrics and network infrastructure to enable the automatic identification and processing of passengers to move them through the airport seamlessly while freeing up staff to concentrate on security threats and customer service.

While the full ambition of assigning a single biometric identifier to a passenger’s entire airport journey, from booking, to check-in, bag drop through to security and eventually boarding is yet to be realised, key elements are already being implemented by airport operators.

2.3.1 The Problem

Many airport terminals have a single common departure lounge for both domestic and international passengers. Here exists the potential for a departing domestic traveler to swap boarding cards with an arriving international traveller, thereby enabling the arriving traveller to transit to a domestic airport and bypass immigration processes.

2.3.2 The Solution

This problem can be remedied by introducing automatic gates with face recognition at the entry to the common departure area and at the gate prior to airplane boarding. The automated gate at plane boarding captures the passenger’s face and verifies it with the face captured and associated with the boarding card when the passenger entered the departure area, thereby detecting if a boarding card has been swapped.

2.4 Surveillance: Real Time Watchlist Alerts

Matching faces captured from CCTV against photographic databases has long been the holy grail of face recognition. These systems are now being deployed today.

2.4.1 What it Delivers

These solutions are designed to integrate with existing surveillance processes; faces are extracted in real-time from the CCTV video feed and matched against a watchlist of individuals. When the system identifies an individual of interest, it raises an alert that can be responded to rapidly and effectively.

In this application of face recognition:

  • FAR represents the percentage of people captured by a CCTV camera that are falsely matched against the watchlist (in essence the number of false alarms raised by the system).
  • FRR represents the percentage of people captured by a CCTV camera who are in the watchlist but for which no alarm is raised.

The alerting mechanism is a binary process. If the system raises too many false alarms, it will quickly be ignored by those tasked with responding to these alerts. The objective of these systems is to minimise the false alerts to a manageable level, while detecting the highest possible percentage of people moving past the cameras who are in the watchlist (true ID rate).

2.4.2 Challenges

It is essential that expectations are set appropriately. Scenarios where thousands of cameras are scanning large crowds of people in day and night environments and from a distance to identify individuals of interest are still largely unrealistic. The best results are obtained:

  • Using newer high definition cameras (3-5 megapixels).
  • Indoors with uniform lighting or outside during daylight in the absence of specific glare.
  • Where people are generally facing the same direction and moving towards the camera.
  • In a suitable pinch-point, such as in a corridor, lane or access gate / turnstile (not large crowds of people).
  • Where cameras are positioned in such a manner as to minimise the angle to the face (ideally < 20 degrees).

Additionally, as the system is comparing poorer quality photos captured from CCTV, it is imperative that the highest quality reference photos are inserted into the watchlist. Systems comparing poor photos against poor photos operate at significantly reduced accuracy levels.

Even with the above considerations in mind, there exist substantial opportunities and environments in which these solutions may be deployed to deliver significant results.

2.4.3 Technical Considerations

These solutions are typically deployed in environments where large numbers of people may be crossing the cameras. As such, depending on the size of the watchlist, a very large number of face verifications need to take place. Such solutions potentially require intensive use of server infrastructure.

Typically, the main considerations that determine the server infrastructure required are:

  • The size of the watchlist.
    (Typically, these would only contain key or significant individuals.)
  • The number of people moving across the camera(s).
    (This represents the number of transactions or searches against the watchlist.)
  • The response time required in which to raise an alert.
  • The number of frames per second which are being captured by the cameras.
    (The higher the frame rate, the more times you capture the same person walking past the camera.)

Real-time searching of an entire criminal database is not typically feasible; consideration should be taken when determining who should be inserted into the watchlist to minimise its size. Typical watchlist sizes are in the hundreds or thousands.

The two major areas of processing inherent in such a system include:

  • Creating biometric templates of all the faces moving across the CCTV camera.
  • Matching these biometric templates against the watchlist.

Of these, template creation generally requires the most CPU power and time.

Therefore very careful consideration must be given to the number of frames per second (fps) the cameras are running at. Many systems typically run at 5-10 fps. While the processing power is significantly reduced, so is the overall accuracy of the system. The lower the fps, the more likely it is that the system will throw away frames containing a high quality image of the individuals’ faces.

To obtain optimal accuracy, cameras should be running at up to 20fps. However, this will result in more images of the same person being captured, resulting in a higher level of template creations and searches. Solutions must be designed with scalability in mind, allowing the most efficient use of server power available.

2.4.4 An Example

An example of an existing live deployment in an airport environment consists of:

  • Up to 10 five megapixel cameras running at 25 fps.
  • A peak transaction rate of 1,000 people per minute moving across the cameras.
  • A watchlist of up to 1,000 people.
  • An alert response time of 5 seconds.

Each person is captured tens of times, resulting in tens of thousands of template creations per minute and tens of millions of biometric verifications per minute.

In this environment, assuming suitable environmental conditions and positioning of the cameras, this system identifies people in the watchlist up to 90% of the time (true id rate) with only one false alarm per day. If operators are willing to accept more false alarms, the true id rate can be increased by configuring system tuning parameters and lowering matching thresholds.

Such systems are already running today.

2.5 Surveillance: Forensic Video Analysis

The increase in the use of CCTV cameras has led to an ever increasing volume of archived video footage. The intelligence in this footage typically remains inaccessible unless appropriately analysed and indexed. Reducing investigation hours when limited resources are available is essential. Such systems can be used to populate databases of “seen” individuals, thereby enabling authorities to search for specific people of interest to determine if, when and where they have been present.

2.5.1 How it Works

  • Faces of individuals are captured from CCTV and archived in a database.
  • Authorities can search the archive using a photo to determine a camera ID and timestamp.
  • Playback of the relevant recording can be enabled by storing pointers into the video archive.

2.5.2 Usage Example: Passengers without Documentation

One usage already deployed today is to quickly and accurately determine the point of origin of arriving passengers without documentation, such as asylum seekers.

If a passenger presents themselves to immigration without documentation and does not provide accurate or complete information about themself, authorities can capture a photograph of the person and search the database of archived faces. If cameras are placed in aerobridges to record disembarking passengers, it is then a simple process to identify on which flight the passenger arrived.

2.6 Queue Management and Flow Analysis

It is becoming increasingly important for airlines and airport operators to monitor queue lengths and passenger flows within the airport. Understanding peak and quiet times is essential to enable sufficient and efficient staffing and resourcing. Raising alerts to manage unforeseen queues is critical for ensuring passenger satisfaction as well as for ensuring that all SLAs with other stakeholders, such as airlines or government agencies, are adhered to.

A common solution thus far has involved the tracking of bluetooth enabled devices such as PDAs and smartphones which are carried by passengers. However, relatively low percentages (approximately 15%) of passengers carry such a device, let alone have the bluetooth on the device activated.

A solution that provides a much more comprehensive data set and accurate information is needed.

2.6.1 The Application of Face Recognition

Solutions using CCTV with face recognition can timestamp when individuals are detected at known camera locations, thereby providing highly accurate information on passenger flows such as:

  • How long does it take to move between two or more points? (such as check-in to security)
  • What are the averages and when are the peaks?
  • How does this vary with time of day?

…as well as providing invaluable insight on how passengers move through the airport:

  • What percentage of passengers move from security to duty free?
  • How many of these are male / female?
  • How long does the average passenger spend shopping in duty free?
  • How is this impacted by queue lengths?

Importantly, no specific passenger identifying information is recorded.

2.6.2 How it Works

As passengers enter an area of interest they are acquired by a camera and anonymously enrolled into the system:

  • CCTV cameras enabled with biometric technology are installed at appropriate areas of interest.
  • Passengers are automatically searched against the database of enrolled individuals.
  • The passenger’s anonymous record is updated with a camera number and timestamp.
  • The database is automatically purged as required at regular pre-defined intervals.
  • The system can raise the appropriate alerts as required (i.e. queues too long).

3 Privacy Considerations

Any article on face recognition would be seriously remiss without at least mentioning privacy. There are a multitude of sources available for detailed discussions on privacy versus benefit of this technology, including the views of this article’s author; readers should familiarise themselves with this issue before considering any deployment of face recognition.

4 What’s Next?

As the use of face recognition continues to be substantiated, more ingenuitive applications will be deployed. Cloud-based services will enable the transfer of expensive computing power out of the airport into shared server facilities. Face recognition will assign a passenger a single unique and transient identity during their movement through the airport, thereby allowing them to be processed by multiple applications seamlessly and effortlessly. Passenger movement through an airport environment can be tracked up to the point of their departure. Personalised way-finding solutions can guide individual passengers to their specific gate, thereby reducing flight delays and passengers who are delaying flights can be quickly and easily located.

6 Summary

The accuracy of face recognition has increased dramatically over the past years. It is now capable of providing reliable results in real-world environments and the technology is being deployed today in airports to enable everything from automated immigration processes, improved surveillance and security, seamless passenger travel and the gathering of valuable statistical information pertaining to passenger movements. The number of potential applications of this technology will continue to deliver benefits in creative ways we have yet to imagine.

The business benefit is real and quantifiable.

This is an excerpt of the author’s original version of a work that was accepted for publication in Biometrics Technology Today (BTT).


Leave a comment

Logged in as allevate. Log out?


Cloud-Based Face Recognition now Accessible from Smart Mobile Devices to Enhance Public Safety in Europe 1

newsrelease

Allevate is now offering Tygart’s new MXMOBILE™ FaceID System for smart mobile devices to enable European government and law enforcement agencies to access an MXSERVER™ system to identify faces on the move.

 

London, UK 6th August 2015– Allevate Limited today announced that, working cooperatively with Tygart Technology, it is now offering Tygart’s new MXMOBILE™ FaceID System for smart mobile devices to enable European government and law enforcement agencies to access an MXSERVER™ system to identify faces on the move. MXSERVER is a cloud-architected system that processes vast quantities of video and photo collections to transform these digital assets into searchable resources by using face recognition.

By leveraging the power and benefits of modern mobile technology, the MXMOBILE FaceID System brings the same power and face recognition analytic capabilities of MXSERVER onto smart mobile devices to lower the cost and improve the accuracy of remotely identifying individuals in the field.

This capability enables an officer located anywhere in the world to capture and upload a photograph to determine the identity of a subject within seconds. “MXMOBILE represents a huge technological leap forward for agents in the field, providing them with the capability to identify individuals using facial recognition in virtually real-time,” says John F Waugaman, President of Tygart Technology.

Agents can now transmit photos or videos captured on their smartphone through the MXMOBILE application, to be processed by MXSERVER using automated face detection and recognition technologies. The faces in the photos or videos are then matched by MXSERVER against watch lists to almost instantly offer a short, rank-ordered list of options that best match these faces, along with any other relevant information such as biographical information, known aliases and previous comments regarding the individual.

In addition to field use for the identification of persons of interest (POI), law enforcement agencies can make MXMOBILE available as a citizen policing tool, providing citizens the ability to upload videos and photographs of suspicious behaviour.

“Allevate has been working to make the power of MXSERVER, already utilised by defense and law enforcement agencies in the USA, available to European agencies”, says Carl Gohringer, founder of Allevate Limited. “We are pleased to be able to offer MXMOBILE to put this capability directly into the hands of law enforcement officers on the move.”

—ENDS—               

About Allevate Limited

Founded in London in 2007, Allevate works with law enforcement, intelligence and government agencies to enhance public safety by ensuring positive identification through the application of biometric and identification technology.

Our relationships with best-of-breed technology suppliers and our extensive network of trusted industry experts coupled with our market intelligence and knowledge of trends ensures we are well positioned to deliver solutions that:

  • Ensure Positive Identification
  • Enhance Public Safety
  • Reduce Operational Costs

Visit us at http://allevate.com, email us at contact@allevate.com, call us on +44 20 3239 6399, follow us on Twitter at @Allevate and follow us on LinkedIN at :http://www.linkedin.com/company/allevate-limited.

About Tygart Technology      

For more information, visit http://www.tygart.com or call +1 304 363 6855.


Video:Enhancing Public Safety with Automated Media Analysis

 

Allevate Presents MXSERVER from Tygart Technology

Security concerns are increasing. Incidents of public disorder and organized crime are on the rise.

The challenges for security services grow more complex. The 7/7 and Boston bombings vividly illustrated the impact of smaller, less sophisticated and more fragmented extremist activities.

Simultaneously, Governments are implementing the most severe budget cuts of recent times. In this landscape, technology can play an increasingly vital role in more efficiently enhancing public safety.

Our security services are faced with a relentless increase in digital media – from police body cameras , online sources such as Facebook and YouTube, confiscated phones and computers and, increasingly, “crowd-sourced” from members of the public.

Allevate is offering MXSERVER from Tygart Technology, a solution that can ingest, analyse and index huge quantities of video and photo media – identifying and highlighting useable intelligence. Trained investigators are freed to intelligently apply their skills without having to view countless hours of media.

Working with Allevate, our security services can more efficiently enhance public safety. We help unlock the intelligence within the vast amounts of media available to police faster than ever before, freeing them to focus on what they are trained to do best – solving and preventing crime and terrorism.


Allevate is Pleased to be Presenting at the 2014 Counter Terror Expo Conference

… in the Practical Counter Terrorism Conference, Day 2, 301th April, 2014

 

Countering the Terrorist Threat via Digital Media Analysis

  • Exploiting digital media to enhance public safety whilst reducing operational budgets
  • Easy and cost-effective routes to access the intelligence in digital media held by law enforcement and intelligence agencies
  • Using face recognition technology to depict individuals of interest

http://www.counterterrorexpo.com/page.cfm/Link=294/nocache=18122013

 


Incredible Talent Now Working With Allevate

I’m incredibly pleased with the array of talent that is now cooperatively working with Allevate.

Today’s announcement detailing the individuals that are supporting Allevate’s mission to enhance public safety through the application of identification technologies whilst improving the operational efficiency of law enforcement and government agencies reflects on the powerful benefits our solutions can provide.

Amazing biomotric technology is not enough. A scalable and proven cloud-based architecture that blends the matching algorithms in a manner that adopts to the forensic investigation workflow seamlessly, coupled with deep insight of customer challenges and processes, is required to ensure maximum benefit.


Article: Intelligence and Efficiency through On-Demand Media Analysis using Face Recognition

You can download a PDF copy of this article by clicking this link.

Governments are implementing the most severe budget cuts of recent times. Against this backdrop, threats from terrorism, organised crime and public disorder continue to rise. Yet recent statistics in the UK demonstrate that authorities can remain resilient and still ensure law-and-order. The targeted application of technology can further increase resilience and the readiness to respond to major events. The relentless advance in the accuracy of face recognition technology, increase in the availability of digital media and mass availability of cheap computing power now provide unique opportunities to meet challenging budgets by drastically enhancing the operational efficiency of forensic investigators while even further enhancing public safety. Digital media can be bulk-ingested in an automated fashion to be processed in a cloud computing environment to identify and extract potential actionable intelligence. Processing is continuous, consistent and predictable. Multiple identification technologies can be deployed and the most suitable algorithms integrated to meeting evolving requirements. Analysts can now focus on investigating and confirming suggested results rather than having to manually watch countless hours of media in the hope of stumbling across the required information. Expanding beyond traditional sources of media is increasingly being accomplished by engaging the public and crowd-sourcing intelligence in response to incidents.

Having previously written on the subject of the application of face recognition in airports[i] and privacy concerns of face recognition when used by retail[ii], this article focusses on the application of face recognition to support bulk processing of media by what has traditionally been the first and thus far most proliferate user of biometric technologies: law enforcement. The convergence of multiple advancements now provides a whole new set of opportunities to use identification technologies in manners that provide benefits that are only now being realised.

1.A Need for Enhanced Safety and Operational Efficiency

Austerity is the order of the day and public budgets are being slashed. Against this backdrop, security risks are continuously increasing. The threat from terrorism, organised crime and public disorder is not abating. Indeed, as reported by the BBC News on the 17th July 2013[iii], the threat landscape is “substantial” and becoming ever more fragmented, consisting of a greater number of smaller and less sophisticated plots.

However, the UK’s police forces have demonstrated that it is possible to maintain and even improve upon public safety despite the relentless pressure of austerity. Recent reports indicate that crime in the UK is at an historic low, being at its lowest level since 1981 [iv]. As always, it appears that necessity is the mother of invention and it is likely that technology is playing an innovative role in improving police efficiency.

What is not apparent from these recent reports, however, is the current level of readiness to respond to a major event. Indeed, the UK’s Police Federation, the body representing rank and file police officers, warns that the police “could not handle more riots”[v] after the budget cuts and Her Majesty’s Inspectorate of Constabulary (HMIC) warns that neighbourhood policing risks being “eroded”. [vi]

There is a need to enhance public safety whilst reducing public operational budgets.

2. A Relentless Increase in Digital Media

The increase in the creation of digital media is relentless. Law enforcement and intelligence agencies have amassed large collections of biographical, video and photographic information from multiple sources such as:

  • Computer hard drives.
  • Mobile phones and portable cameras.eye
  • Flash memory devices.
  • Online sources on the Internet such as Facebook and YouTube.

Additionally, when tragic events or social disorder occur, investigators have a long and arduous task of reviewing countless hours of CCTV footage, generally with a varying degree of concentration and scrutiny.

A solution that minimises manual effort in the extraction of actionable intelligence from amassed media by automating this process with a consistent and repeatable level of scrutiny will deliver concise and consistent information in a fraction of the time taken by operators undertaking the task completely manually.

3. An Automated Media Processing and Exploitation Solution

Police, intelligence and other public order agencies would benefit from the application of a powerful media processing solution designed to process, ingest, analyse and index in an automated fashion very large quantities of photographs and videos to transform them into usable assets. 

Allevate is offering MXSERVER[vii], already operationally proven with US Federal agencies, to EU agencies to address the issues raised in this article..

Such an automated solution ingests and processes media from multiple sources. Once processed, law enforcement agencies can analyse and make use of the extracted assets and manage them in a centralised repository of information. Data links, associations and metadata inferences can be managed across the whole dataset by multiple users from a single common user interface. Backend processing services are run in a cloud-computing environment, the capacity of which can be configured and incrementally scaled up and down to meet an organisation’s changing demands; peaks arising from specific events can be easily accommodated.

Features include:

  • Automatically find, extract and index faces to enable biometric and biographic searching of media.
  • Create and manage watchlists of people of interest via a web-based interface.
  • Find and cross-reference all media instances in which a person of interest has been seen.
  • Identify, locate, and track persons of interest, their associates and their activities across all media.
  • Discover, document and view links between people of interest, their activities and networks.
  • Use of metadata (including geo data) in the media to enhance investigations and association of data.
  • Integration into existing system environments, databases and components via a flexible API.

 

3.1  Incorporating Other Detection Capabilities

In addition to face detection and recognition, other detection engines can be incorporated, such as:

  • Automatic Number Plate Recognition. (ANPR)
  • Voice Biometrics.
  • Object / Logo Recognition.
    (Other identifying features can be used to track individuals through other processed media.)
  • Scene Recognition
    (Identify similarities in the entire frame, often used in child-exploitation investigations)

Vendor independence allows the use best-of-breed algorithms.

3.2  Biographic Filtering and Fuzzy Match Capability

Forensic investigations are complex and require a holistic view of all available data. This involves not only analysing media, but making full use of all textual and biographic data available as well. This can include text from files recovered from hard drives and other storage devices, online sources, metadata associated with photo or video files and data entered by investigators during the investigation.

Traditional Boolean search techniques only work within a black and white, true and false paradigm. More applicable within a complicated forensic analysis are techniques that use advanced “fuzzy” algorithms that to calculate similarities and aggregate match scores using multiple criteria to enable a “shades of grey” analysis.

Such an approach can fuse match scores across multiple disparate search criteria and even allows for fusion and aggregation of search results across multiple biometric and biographic criteria.

The use of media metadata and other biographic data further refines biometric matching.

3.3  Working with Geo-Location Data

An ever-increasing amount of media available to investigators is captured on mobile devices and cameras affixed with location determining technology. This includes media obtained from CCTV, confiscated hardware and devices, online sources and voluntarily made available by members of the public. The majority of the time, this geo-location data is incorporated into the media metadata, thereby providing significant potential to further enhance the analysis of the media. For example, geo-location can be used to:

  • Compartmentalise and refine analysis by location of where the media was created.
  • Overlay location of proposed matches onto maps.
  • Chart movements of individuals of interest by location and time of sightings.
  • Link individuals at the same location and time even if they do not appear together in media

3.4  Architecture and Integration with Existing Systems

There are significant similarities in organisation and methods of operation in many western law enforcement agencies facilitating increased levels of co-operation. Operational systems should support full control of information and data as well as have sufficient in-built flexibility to enable authorised data exchanges.

In addition to utilising COTS components, adhering to common standards and being cloud-architected to enable massive scalability, a well delineated scope of functionality and open API enables:

  • Flexibility in customisation and integration with existing systems and workflows.
  • Well-defined mechanisms of loading data and automating ingestion of media for processing.
  • Dynamic alteration and sharing of watchlists, media, system-generated results and operator analysis.

3.5  Hosting, Cloud and Virtualisation Options

Full architectural flexibility enables flexibility of hosting options. Organisations can elect to:

  • Take advantage of IaaS and SaaS options on cloud offerings.
    (UK accreditation of IL0 to IL3 is available via hosting partners)
  • Fully host the solution on their own private and secure premises and datacentres.
  • Deploy in a hybrid manner.
    (Thereby taking advantage of external processing power whilst retaining the most secret data) 

3.6  Working Hand-in-Glove with Trained Forensic Investigators

The human operator will always remain the critical and essential part of intelligence analysis; media analysis solutions are not designed to replace the intricate skills and knowledge of trained investigators. Rather, the operator is enabled to intelligently direct and apply their extensive training at suggested results, eliminating the necessity of rote viewing of countless hours of media either in a sequential our random fashion.

Integration of enhanced verification, charting and mapping tools enables operators to conduct detailed analysis of suggested matches and identifications to confirm or deny them.

4. Potential Use Cases

There are myriad different applications of a solution architecture as described herein within military, law enforcement, intelligence and public site security agencies. These are summarised into three broad categories:

4.1  Time Critical Investigations, Media of Critical Importance

In certain major incidents, timeliness of response is of the essence. Authorities need to quickly process evidence to identify and apprehend individuals. The scale of the investigation is often huge and the amount of media that needs to be processed massive. Examples include terrorist events such as the recent Boston bombing and the Woolwich attack in South London.

Often, the media acquired in these instances is of such critical importance that the authorities may choose to review it all in its entirety, frame-by-frame. However, in the early stages after the incident, decisive and immediate action is critical. Rather than having to sift through the media in a random or sequential fashion, a media analysis solution can quickly direct the investigators to the portions of the media that are most likely to deliver immediate results. Full review of the media can be conducted during subsequent stages of the investigation.

4.2  Bulk Ingestion of Media Arising from Criminal Investigations

During routine operations or specific criminal investigations, authorities may recover significant quantities of media on confiscated hard drives, mobile phones, flash / thumb drives and other sources that need to be processed to either further the investigation or to assist in building an evidence base for criminal prosecution.

Examples include:

  • Military or counter-terror officers raiding terrorist training facilities.
  • Specialist organised crime investigators raiding the offices of organised crime syndicates.
  • Child protection officers raiding premises of individuals or organisations involved in child exploitation.

This media can be bulk ingested in an automated fashion to provide the investigating officers an overall summary of the contents including focus areas for further investigation.

4.3  Continuous Background Processing of Media Sources

Authorities may as a matter of routine have access to masses of media which may contain actionable intelligence, but typically would never be viewed or processed due to a lack of resource and the time consuming nature doing so. Examples include:

  • Media from specific cameras installed at high-profile or sensitive locations.
  • Media from known or suspect online sources or accounts from social media sites.
  • Media made available to the authorities by the general public.

Intelligence in these sources may be missed entirely and never acted upon.

This media can now be bulk ingested and processed in an entirely automated fashion to flag any relevant intelligence, using operator controlled criteria, to the authorities as required for follow-up processing.

5. A Compelling Business Case

The solution and optional IaaS / SaaS components can be made available on a monthly service-charge basis, thereby requiring a minimal capital outlay and enabling a compelling operating expenditure business model.

Whilst the human operator is an essential part of intelligence analysis, an entry level solution empowers the analyst to process up to an order of magnitude more media on a daily basis. This enables trained operators to apply their expertise and training by focussing on the analysis of results generated by the solution in a more focused effort than manually watching hour upon hour of media.

Efficiency is dramatically boosted by bulk processing media 24×7 at a constant and predictable level of focus and accuracy: operational staff can focus on analysing results.

6. Engaging the Public to Crowd-Source Media to aid Investigations

Increasingly, especially from crowded public events, authorities are making greater use of media captured intelligence in the form of photographs and videos that have been recorded by members of the public.

With the advent of smartphones, almost everybody has a high quality camera in their pocket.

Most members of society would welcome the opportunity to assist the authorities with their investigations, but often do not know how or are fearful of being involved.

Allevate’s proposed PublicEye service is aimed at empowering the public to take a greater collective social responsibility and assist law enforcement in much the same manner as the phenomenally successful CrimeWatch. It enables members of the public to (at their discretion) upload media directly from their mobile phone or other internet device to a public portal for processing and dissemination to the relevant authorities.

A PublicEye portal could be used:

  • In response to appeals by the police to the public who were present at an event or disturbance.
  • When individuals witness a crime being committed.
  • Upon suspected sightings of missing persons or individuals wanted by the authorities.

A PublicEye enables the authorities to crowd-source media to augment their own sources.
 

7. Summary

In the UK, MXSERVER is available on the G-Cloud catalogue [viii]which is designed to provide a streamlined process for buying ICT products and services as a commodity without having to invite tenders from suppliers.

Security concerns are ever increasing. However, public budgets are being slashed. Law enforcement agencies are rising to the challenge of implementing budget cuts partly through the focussed application of technology. The accuracy of face recognition has increased dramatically over the past 10 years. This, coupled with the massive increase in the creation of digital media and the availability of cheap computing, now provides authorities with the ability to bulk ingest and process media in an automated fashion. Results are continuous and predictable. Trained analysts can now focus their skills on investigating suggested results and on intelligence extracted by automated systems. Not only does this provide the ability to process critical media even faster than ever before to respond time critical investigations, but it also enables authorities to extract intelligence from media sources that in the past may never even have been looked at because of the significant resource this previously would have entailed.

Additionally, the availability of smartphones means almost everybody is carrying in their pocket a high quality camera. The ability to process media rapidly and cheaply means the authorities will be able to, on a continuously increasing basis, engage with members of the public to crowd-source media in response to major investigations.

8. About the Author

Carl is the founder of Allevate Limited (http://allevate.com), an organisation that works with law-enforcement, intelligence and government agencies to enhance public safety by ensuring positive identification through the application of biometric and identification technology. With over 20 years’ experience working in the hi-technology and software industry globally, he has significant experience with identification and public safety technologies including databases, PKI and smartcards, and has spent the past 10 years enabling the deployment of biometric technologies to infrastructure projects. Carl started working with biometrics whilst employed by NEC in the UK and Allevate subsequently supported NEC’s global and public safety business internationally.

Residing in the UK since 1993, Carl was born and raised in Canada and holds a Bachelor of Science Degree on Computer Science and Mathematics from the University of Toronto.

You can download a PDF copy of this article by clicking this link.

 Follow us on Twitter: @Allevate

2,774 words


 

[i] Allevate, July 2012:

[iii] BBC News, 17th July 2013
http://www.bbc.co.uk/news/uk-23334719

 

[viii] The UK’s G-Cloud Programme is a cross government initiative led by Andy Nelson (Ministry of Justice) supported by Denise McDonagh (Home Office) under the direction of the Chief Information Officer Delivery Board as part of the Government ICT Strategy.

http://allevate.com/news/index.php/2013/05/13/face-recognition-media-exploitation-system-g-cloud-iii-cloudstore/

 


Find People Fast in Media using Cloud-Based Face Recognition during Forensic Analysis

When tragic events or social disorder occur, forensic investigators have a long and daunting task of reviewing countless hours of CCTV footage. Increasingly, especially at public events attended by large numbers of people carrying mobile phones with HD cameras, authorities rely on  members of the public to turn in photographs and videos they have taken in the hope that they will contain useful intelligence. Much of this media is already uploaded to public sites such as Facebook and YouTube, providing another rich source of information.

Additionally, police have to review countless hours of media obtained from confiscated computer hard drives, mobile phones and portable cameras and flash memory devices.

Face Recognition?

All of this creates a significant resource burden;  this footage must be watched by people. The application of face recognition technology can play a crucial role in identifying potential suspects.

An Automated Media Processing Cloud

A solution to automate the processing of this staggering amount of media to quickly and efficiently unlock actionable intelligence is required to save significant time and human capital. The ability to automate this would allow the more efficient application of resources as well as massively speed up time-critical investigations.

However, the need goes far beyond the simple application of face recognition technology.

What is needed is a server-based system that can process vast amounts of media quickly to transform files from  mobile phones, flash memory devices, online sources, confiscated computers and hardrives and video surveillance systems into searchable resources. This would enable forensic investigators to work more efficiently and effectively by automatically finding, extracting and matching faces from very large collections of media to discover, document and disseminate information in  real-time.

Such a powerful video and photograph processing architecture should automatically ingest, process, analyse and index hundreds of thousands of photographs and videos in a centralised repository to  glean associations in a cloud environment. Instrumental would be the ability to:

  • Automatically find, extract and index faces to enable  the biometric and biographic searching of media.
  • Create and manage watchlists of people of interest via a web-based interface.
  • Find all instances of photos and videos where a person of interest has been seen.
  • Quickly review and process  media to identify, locate, and track persons of interest, their associates and their activities.
  • Discover, document and diagramtically view  associations between people of interest, their activities and networks.
  • Use media meta-data to geotag video footage and watchlist hits and overlay and present on maps.

Public Facing Cloud-Service to Crowd-Source Media

Finally, a public-facing interface to such a system would enable members of the public to upload their media in a self-service manner to enable quick and ready access by the authorities to this raw data for automatic processing.

Enhance Public Safety and Reduce Budgets

Read about how MXSERVER addresses the AMAIS space (Automated Media Analysis for Intelligence Searching)

This solution is now available to UK public sector on the Government Procurement Service CloudStore – G-Cloud iii Framework as a commodity from the catalogue without having to invite tenders from suppliers.

 


Could Automating Media Processing Aid the Forensic Investigation into the Boston Marathon Bombing?

The horror of the events at the marathon in Boston 2 days ago is still very raw. People are united in their sympathy for the victims and their families, their revulsion of these despicable acts and their solidarity in not succumbing to terror. The FBI vows to “…go to the ends of the Earth to find the bomber” with President Obama openly stating the “…heinous and cowardly…” event to be “…and act of terror”.

The investigation into the bombing is in its nascent phases, with the Boston Police Commissioner Ed Davis admitting that they are dealing with the “…most complex crime scene that we have dealt with in the history of our department.” Still, authorities are already honing in on crucial evidence and beginning to release details; BBC news reports that a source close to the investigation told AP news agency that the bombs consisted of explosives placed in 1.6-gallon pressure cookers, one with shards of metal and ball bearings, the other with nails, and placed in black bags that were left on the ground. Images of what appear to be a trigger mechanism have already been released.

Face Recognition?

Forensic investigators have a long and daunting task ahead of them with countless hours of CCTV footage to  pore over, and some people are already suggesting that the application of face recognition technology can play a crucial role in identifying potential suspects. However CCTV footage, especially from older systems that have not been specifically configured for the task, is notoriously unreliable as a source for face recognition.

Perhaps more useful at an event attended by so many, most of whom will have been carrying and using mobile phones and cameras, is the footage acquired by members of the public. Images and video captured by these high-quality devices will potentially be of much greater use than CCTV and authorities have appealed for people to turn in photographs and videos they have taken in the hope that they will contain useful intelligence. Much of this media will already have been uploaded to public sites such as Facebook and YouTube.

 An Automated Media Processing Cloud

A solution to automate the processing of this staggering amount of media to quickly and efficiently unlock actionable intelligence is required to save significant time and human capital. The ability to automate this would allow the more efficient application of resources as well as massively speed up a time-critical investigation.

However, the need goes far beyond the simple application of face recognition technology.

What is needed is a server-based system that can process vast amounts of media quickly to transform files from  mobile phones, flash memory devices, online sources, confiscated computers and hardrives and video surveillance systems into searchable resources. This would enable forensic investigators to work more efficiently and effectively by automatically finding, extracting and matching faces from very large collections of media to discover, document and disseminate information in  real-time.

Such a powerful video and photograph processing architecture should automatically ingest, process, analyse and index hundreds of thousands of photographs and videos in a centralised repository to  glean associations in a cloud environment. Instrumental would be the ability to:

  • Automatically find, extract and index faces to enable  the biometric and biographic searching of media.
  • Create and manage watchlists of people of interest via a web-based interface.
  • Find all instances of photos and videos where a person of interest has been seen.
  • Quickly review and process  media to identify, locate, and track persons of interest, their associates and their activities.
  • Discover, document and view  associations between people of interest, their activities and networks.

Finally, a public-facing interface to such a system would enable members of the public to upload their media in a self-service manner to enable quick and ready access by the authorities to this raw data for automatic processing.

 


Unlocking Intelligence from Multi-media

Driven by growing security concerns arising from increasing terrorist attacks, racial and ethnic disturbances, organised civil unrest, random violence, riots, burglary and physical assaults, the global market for the face and voice biometric technologies is projected to reach US$2.9 billion by the year 2018.

Across Europe, governments and law enforcement agencies are increasingly impotent in their ability to combat a deterioration in public safety. The economic crisis that is increasingly fueling public disorder is also paralysing our police and intelligence agencies with draconian budget cuts.

Having previously invested heavily in infrastructure, these agencies have at their disposal huge volumes of data in the form of media, but have no way to unlock the potential intelligence bonanza it contains. Vast sums are being spent allocating experienced and expensive human capital to rote tasks of watching countless of hours of media in the hope of randomly finding useful information.

A solution to automate this processing to quickly and efficiently unlock actionable intelligence from this staggering amount of data is required. The potential to improve public safety whilst simultaneously enabling the more efficient use of our public finances is huge.


Turn Masses of Video in Archives into Actionable Intelligence

There has been an explosion in digital media. Law enforcement and intelligence agencies have amassed large collections of video and photographs from multiple sources that are stored in multiple file formats. There is a need to automate the processing of this raw data to turn it into actionable intelligence to enable you to “connect the dots”.

Discover how solutions available from Allevate can dramatically save you time and help you to operate more efficiently by appsurveillancelying data mining principles to digital media:

  • Automatically find and match faces from huge stores of videos and photos.
  • Identify individuals from watchlists and track them across multiple videos.
  • Extract faces from video and automatically cross-reference with all other video.
  • Associate multiple videos and photos based upon their active content and the individuals they contain.
  • Apply enhanced link analysis to identity an individual across multiple video sources.
  • Automatically build links between different individuals based on their associations in media, whether they be known or unknown.
  • Automatically and graphically display web-based drill down link analysis diagrams.
  • Determine “Pattern of Life” analysis for specific individuals and flag deviations from the norm.
  • Manage and access your entire video and photo repository from a single web interface. (automatically transforming multiple video formats)
  • Apply powerful analytical tools to your digital media content.

Work more efficiently. Get more results. Exploit the masses of raw media from multiple sources to create actionable intelligence with less manpower.


Article: Face Recognition: Profit, Ethics and Privacy 2

You can download a PDF copy of this article by clicking this link.

The accuracy of face recognition has increased dramatically. Though biometric technologies have typically been deployed by governments and law enforcement agencies to ensure public, transport and border safety, this improvement in accuracy has not gone unnoticed by retailers and other commercial organisations. Niche biometric companies are being snapped up by internet and social media behemoths to further their commercial interests, and retailers and other enterprises are experimenting with the technology to categorise customers, analyse trends and identify VIPs and repeat spenders. Whilst the benefits to business are clear and seductively tantalising, it has been impossible to ignore the increasing murmurs of discontent amongst the wider population. Concerns over intrusion of privacy and the constant monitoring of our daily lives threaten to tarnish the reputation of an industry which has endeavoured to deliver significant benefit to society through improved public safety. Can the industry be relied upon to self-regulate? Will commercial enterprise go too far in their quest to maximise profits? How far is too far? How can organisations ethically make use of face recognition technology to increase efficiencies and drive revenue, whilst respecting and preserving privacy and maintaining the trust of their clientele and society?

Having previously written on the subject of the application of face recognition in airports as applied by law enforcement and border control, this article looks at the increasing exploitation of the technology for commercial advantage. As well as contrasting the different use-cases defined by commercial exploitation versus public safety applications, this article also touches upon the very different agendas of those using the technology and the privacy issues that arise.

1  Advances in Face Recognition Technology

Face recognition is increasingly transforming our daily lives. A study by the US National Institute of Standards and Technology (NIST) in 2010 demonstrated that the technology has improved by two orders of magnitude in accuracy over 10 years and further tests currently being conducted by NIST are expected to demonstrate its continued relentless advance. Those interested in reading of these astonishing improvements are encouraged to refer to “Advances in Face Recognition Technology and its Application in Airports”, first published in Biometrics Technology Today (BTT) in July 2012, which summarises the 2010 NIST results in detail.

2  Public Safety versus Generating Profit

Most people accept that the reality of the world today necessitates certain inconveniences and intrusions. We tolerate and increasingly expect surveillance technology to be deployed wisely in situations where there is demonstrable benefit to public safety, such as at transport hubs, large gatherings, public events or areas of critical national infrastructure. The key factor behind such tolerance is comprehension; we understand the reasoning behind these uses and the benefits to ourselves, namely our safety. Though we don’t necessarily like it, we generally accept it.

However, it has been difficult to avoid the increasing coverage in the media of the use of face recognition by commercial organisations. The single most common term that is bandied about in reference to these deployments tends to be “creepy”. The technology being deployed is very often similar, if not identical to, the technology deployed for public safety applications. So precisely what is it about this use of technology that people are averse to?

In order to understand this, it is useful to consider in each case who people perceive benefit from the system. In the case of public safety, the people perceived to benefit are us; the citizens. In the case of commercial use, people perceive the commercial organisation deploying the technology as the beneficiaries. In this scenario the term “benefit” generally means profit, either by increasing revenues or decreasing costs. Often there is a general distrust within society of large corporations profiting from the exploitation of the populace, and this is especially true in times of prolonged economic difficulty. This is additionally complicated by the fact that our biometric traits are viewed as being something that are intrinsically ours and that are a constituent part of our definition.

3  Examples: Uses to Reduce Cost and Generate Revenue

It hasn’t taken long for business minded technology companies to devise a whole range of new uses of face recognition, all focussed on delivering bottom line business benefit. An important characteristic of face recognition is that it is only useful if you have something to match a photograph (probe) against, whether it is another photograph, or a database of photographs (reference set). It is the management, control of access to and often the creation of these reference sets that generate the most privacy concerns.

Let us briefly discuss some of the manners in which the technology is currently being deployed.

3.1  Efficiently Identifying Customers and Staff

This perhaps is the most traditional use of biometrics within commercial organisations. The ability to positively identify people, whether they are your staff or increasingly your customers, is absolutely necessary for the day-to-day operation of business and indeed society. Biometrics can be applied to ensure identity in a more cost-effective and positive manner, thereby introducing efficiencies into the business. It is an unfortunate reality that staff are responsible for a significant amount of theft. Adopting biometric technology can eliminate password theft and help mitigate the risks of identity sharing, thereby reducing fraudulent and unauthorised transactions and ensuring relevant personnel are physically present at the time of a transaction. Additionally, customers can be identified positively before conducting transactions. Cashless payments provide numerous efficiency opportunities by allowing elimination of cash and credit cards at point of payment altogether.

3.1.1         Privacy Considerations

These examples are usually only possible with the consent and approval of the individuals in question. Customers typically register for a biometric payment system, for example, in order to realise a benefit offered by the enterprise. The enterprise in turn must satisfy the customer that their biometric reference data will be kept and managed securely and only for the stated purpose.

The advent of face recognition provides new manners in which you can identify your customers, for example from CCTV cameras as they enter shops or as they view public advertising displays. It is when these activities are performed without the individual’s knowledge or consent that concerns arise.

3.2  Identifying Who is Entering Your Premises

These solutions are designed to integrate with existing surveillance systems; faces are extracted in real-time from a CCTV video feed and matched against a database of individuals. When the system identifies an individual of interest it can raise an alert that can be responded to rapidly and effectively, or log where and when the individual was seen for the formation of analytical data.

This can be used to provide valuable real-time or analytical intelligence to organisations, such as:retail

  • Notification of the arrival of undesirables, such as banned individuals or known shoplifters.
  • Notification of the arrival of valued or VIP customers.
  • Collation of behaviour data of known customers, such as how frequently they visit, which stores they visit and integration with loyalty programmes.

 

 

 

3.2.1         Privacy Considerations

There are a number of potential issues with regards to privacy that need to be considered here, most notably:

  • How is the reference set obtained? Who is in it?
  • Do you have the permission of the individuals in the reference set?
  • How are the photographs in the reference set stored and secured?
  • Are the members of the reference set aware of how and when their photos will be searched?
  • Are the people crossing the cameras aware that their photos are being searched against pre-defined reference sets?
  • What action is taken if a probe image matches against the reference set? What are the implications of a match or a false match?
  • What is done with the probe images after searching the reference set? Are they discarded or stored?

The number of possible uses of this functionality and resulting business benefits are too large to enumerate here, but very careful consideration must be made with regards to the proportionality of the solution when measured against the requirement. Additionally, the views and considerations of the individuals whose images you are verifying, both the people within the reference set and the people whose faces you are sampling as probe images, should be well understood and considered; approval should be sought for inclusion into a reference set.

3.3  Analysing How People Moving Through Your Premises

Face recognition can also be used to determine how people move through premises, such as a department store. Understanding peak and quiet times is essential to enable sufficient and efficient staffing and resourcing. Raising alerts to manage unforeseen queues is critical for ensuring customer satisfaction.

Face recognition applied to CCTV can timestamp when individuals are detected at known camera locations, thereby providing highly accurate information on people flows such as:

  • How long on average does it take to move between two or more points?
    (such as from the entrance of a store to a checkout or exit)
  • What are the averages flow times across the day and when are the peaks?
  • How does this vary with the time of day?

This can be used to determine how people typically move through the premises, and how long on average they linger in specific areas. You can also analyse this data across different age and gender demographic categories.

3.3.1         Privacy Considerations

Importantly, no person identifying information is recorded. There is no interest in identifying who the individuals moving through the premises are or in taking any specific action on any specific individual. There is no need to search against any pre-defined reference sets.

However, there are some issues you should consider when deploying such systems:

  • Biometric matching of people crossing the cameras still occurs. The probe photos are matched against other anonymous people that have previously crossed the cameras.
  • You should carefully consider how long this data will be retained for matching, (generally hours) and the nature of the premises being monitored.

Generally the privacy considerations of this application are minimal.

3.4  Building Databases of People Visiting Your Premises

As previously mentioned, face recognition is only useful if you have images to match against. Previous examples have dealt with matching the faces of people crossing the camera against known databases of individuals. A potentially far more valuable practice to enterprise is to dynamically build reference databases consisting of the people who cross the camera. Unfortunately, this is also the practice that riles the populace the most and is rife with potential privacy intrusions.

The increase in the use of CCTV cameras has led to an ever increasing volume of archived video footage. The intelligence in this footage typically remains inaccessible unless appropriately analysed and indexed. Such systems can be used to populate databases of “seen” individuals, thereby enabling searching for specific people of interest to determine if, when and where they have been present. This then allows the collation of data such as how frequently individuals visit your premises, how long they stay and when was the last time the individual visited your premises, as well as which of your locations any individual frequents and which is the most common.

If this functionality is combined with the ability to search and cross- reference against databases of known individuals, for example a subscribed customer database, this can then allow you to build very valuable analytical data on specific individuals thereby enabling you to predict future behaviour and market more specific services and products.

3.4.1         Privacy Considerations

Tread very carefully. Some of the most vocal opposition to the application of face recognition technology results from the capture of biometric data of potentially large numbers of people without their knowledge or consent, especially if the people are then identified and profiled against existing databases. In many jurisdictions around the world, the retention of such data may be in contravention of privacy legislation.

3.5  Analysing Who is Viewing What to Target Your Advertising

There have been many examples in recent months of retail and advertising organisations using technology to determine the approximate age and gender of people entering premises or viewing advertising walls. Though not technically face recognition, it is still worth mentioning here as often the distinction between the two uses is blurred. The premise is simple: such solutions can count the number of people watching an advert at any given time, and even estimate their age, dwell time, sex and race. While providing invaluable information for the advertiser, it can also allow them to dynamically change the adverts in real time to more appropriately target the demographic of the current viewer(s). Such solutions are increasingly being deployed in Japan and it is only a matter of time until they are more widely considered in Europe and North America.

3.5.1         Privacy Considerations

The key consideration here is that this form of technology is not actually identifying anybody or extracting personally identifiable information. There does appear to be some opposition to this, though none of it very vocal or serious. It is difficult to see any infringement of privacy and often may be advantageous to the consumer as advertising may be more specifically tailored to their needs.

3.6  Matching People on Your Premises with Social Media Accounts

Both Google and Facebook have acquired face recognition technology companies over the past year. Facebook’s users, for example, publish over 300 million photos onto the site every day, thereby making Facebook the owner of the largest photographic database in the world.

Facebook is already trialling a new service called Facedeals which enables its users to automatically check in at participating retail sites equipped with specially enabled cameras. In order to entice users to participate, the participating retailer can offer special deals to Facebook users when they arrive. The flow of information can be bi-directional. Such automatic check-in data coupled with the users’ manual checkins can be used by Facebook to hone their profile of individuals allowing them to target users with more relevant advertising. The system is entirely voluntarily, and the reference sets searched by retailers only contain photos of users who have opted into the service.

3.6.1         Privacy Considerations

Making data from social media sites available to other commercial organisations is a potential privacy minefield and should only ever be done with users’ consent. Defining these as opt-in services is exactly the right way forward. Likewise the profiling of users of social media sites based upon automatic tagging of images uploaded to those sites should be strictly controlled and only enabled on an opt-in basis. The privacy concerns over such activities have recently been very aptly illustrated by Facebook’s withdrawal of its controversial auto-tagging feature from use in Europe after pressure from privacy campaigners and regulators.

4  Social Media, Cloud Computing and Face Recognition

Dr. Joseph J. Atick of the International Biometrics and Identification Association has written a thought-provoking paper entitled “Face Recognition in the Era of the Cloud and Social Media: Is it Time to Hit the Panic Button?”. The paper raises several interesting points that merit mention here. In it Dr. Atick argues that the convergence of several trends including the:

  • High levels of accuracy now attainable by face recognition algorithms.
  • Ubiquity of social networking with its inherent large photographic databases.
  • Availability of cheap computer processing and the advent of cloud computing.

…coupled with the fact that “face recognition occupies a special place [within the family of biometrics in that] it can be surreptitiously performed from a distance, without subject cooperation and works from ordinary photographs without the need for special enrolment…” is “ … creating an environment … that threatens privacy on a very large scale…”.

One of the main premises of the paper is that this issue “… will require the active cooperation of social media providers and the IT industry to ensure the continued protection of our reasonable expectations of privacy, without crippling use of this powerful technology”.

5  Can All This be Done Ethically? (What About Privacy?)

Can organisations ethically make use of face recognition technology to increase efficiencies and drive revenue, whilst respecting and preserving privacy and maintaining the trust of their clientele and society?

The premise of “privacy-by-design” should be used to ensure that privacy is considered from the outset of any deployment of face recognition technology. In fact, the European Union’s 22-month Privacy Impact Assessment Framework (PIAF) project advises that “Privacy impact assessments should be mandatory and must engage stakeholders in the process” for all biometric projects.

Reputable organisations such as the Biometrics Institute have gone so far as to publish invaluable privacy charters to act as a “…good executive guide operating over a number of jurisdictions…” which should be reviewed and seriously considered before any deployment of biometric technology.

Some of these fundamental principles are outlined below within context of the subject matter of this article and specifically within the context of commercial use of the technology. These will not necessarily apply when discussing matters of public safety, law enforcement and national security.

5.1  Proportionality

A fundamental principle of privacy concerns the limitation of the collection of data to that which is necessary. Organisations should not collect more personal information than they reasonably need to carry out the stated purpose. Biometric data by its very nature is sensitive and absolute assurance must be provided that it will be managed, secured and used appropriately. However, a key consideration in the use of this technology should be proportionality; is the collection of such sensitive data justified for the benefit realised?

5.2  Educate and Inform

People on the whole generally resent not being informed, especially in matters that involve them. History is littered with IT projects that have failed because key stakeholders were not involved from the outset, were not sufficiently informed and whose buy-in to the process was not obtained. Customers are one of the most important stakeholders and these issues are even more critical when dealing with their personal and biometric data.

There is a very interesting video on YouTube that illustrates this point very nicely. It is filmed by a man with a camera walking around filming random strangers without explanation. The reaction is predictably always negative and sometimes hostile. The message the video is trying to make is obvious: most people do not approve of being videoed, so why do we so readily accept surveillance cameras? The message that comes across is actually clearer: People object when they do not understand intent, purpose or benefit to themselves. The cameraman offered no messages of explanation of his intent, even when challenged. Objection was guaranteed.

5.3  Be Truthful and Accurate when Describing the Business Purpose and Benefit

As part of the process of informing, organisations should also be direct and open in disclosing not only the existence of the systems, but the scope, intent and purpose of the solutions. Why are you utilising an individual’s biometric data? What benefit does it serve? What is the scope of the use of this data?

Importantly stay well clear of “scope creep”. All too often it is tempting to start using data once you have it for other than the stated intended purpose for which it was collected. Such endeavours will inevitably lead to loss of trust.

5.4  Provide Benefit to the Customer

Simply understanding the scope, purpose and intent of a system generally will not be sufficient to garner acceptance of the system. While people are generally astute enough to realise that businesses are in the business of making money, they’ll want to know what is in it for them. What is their benefit?

An example with which most of us will be familiar are grocery store loyalty or “club” cards. Whilst we all understand the objective of the grocery store is to profile and analyse our spending in order to better market to us, a majority of us still subscribe in order to receive the enticements and benefits on offer.

Within the context of face recognition, Facebook’s Facedeals programme demonstrates this principle nicely. Users understand the benefit to Facebook and the retailer, yet they still may choose to opt in to the programme because there is a clear and discernible benefit for them to do so as well, namely targeted discounts and offers at retail outlets.

This is also affirmed by a survey in 2012 by IATA which finds that “… most travellers are receptive to the idea of using biometrics within the border control process.” Why? Because there is clear and discernible benefit to them in the form of a more efficient passenger process and increased levels of security.

5.5  Seek Consent and Operate on an Opt-in Principle Where Appropriate

Biometric enrolment into such systems should not be mandatory. Individuals should be allowed the ability to opt-in, with an opt-out status being the default. Clearly this is not always feasible when considering people in public places the crossing cameras. However, if they are being identified against reference sets, the individuals in the reference sets should be there only with consent. Automatic enrolment into reference sets or biometric databases should involve the consent and approval of those enrolled.

Importantly, people should not be penalised should they choose not to opt-in; they should still be allowed a mechanism of transacting and conducting their business.

The recent decision by the UK Department of Education to prohibit schools from taking pupils’ fingerprints or other biometric data without gaining parents’ permission is a prime example of a potential backlash when such systems are made mandatory without providing any alternative mechanism of transacting. In many cases in UK schools, students were left with no mechanism of buying their school lunch unless they enrolled into a biometric system.

6  Summary

The accuracy of face recognition has increased dramatically. Retailers and other commercial organisations are investigating ways to exploit this technology to increase revenues, improve margins and enhance efficiency. Social media companies own the largest photographic databases in existence and are under pressure from shareholders to find ways to monetise these assets. As these explorations gather pace, so does the discontent of privacy advocates.

This article has outlined a number of ways face recognition can be used by enterprise and highlights potential privacy issues. Is it possible to ethically use face recognition technology and respect privacy? This will only be possible if enterprise maintains the trust and respect of its customers. Open and honest discourse is the best manner in which to achieve this. This should be accompanied by delivering real benefit to all parties involved in a manner that also empowers the customer; nobody should be forced to enrol into biometric systems or be disenfranchised from refusing to do so.

How far is too far? History has shown that there is no absolute answer to such questions. The exact location of the line to be crossed is always a factor of and changes with the times we live in. History has also shown, especially as it pertains to technology, that it is next to impossible to put the genie back into the bottle once released. It is now the collective responsibility of all to ensure the proper and ethical use of this technology in a manner that delivers the maximum benefit. This will require the active cooperation of social media, enterprise, the IT industry and civil liberty groups to ensure the continued protection of our reasonable expectations of privacy without crippling the use of this powerful technology. In the end, the people have the loudest voice. If enterprise crosses the line, customers will pass judgement with their wallets. 

7  About the Author

Carl is the founder of Allevate Limited (http://allevate.com), an independent consultancy specialising in market engagement for biometric and identification solutions. With over 20 years’ experience working in the hi-technology and software industry globally, he has significant experience with identification and public safety technologies including databases, PKI and smartcards, and has spent the past 10 years enabling the deployment of biometric technologies to infrastructure projects. Carl started working with biometrics whilst employed by NEC in the UK and has subsequently supported NEC’s global and public safety business internationally.

Residing in the UK, Carl was born and raised in Canada and holds a Bachelor of Science Degree on Computer Science and Mathematics from the University of Toronto.

You can download a PDF copy of this article by clicking this link.

 

3,976 words

 ————————————————————————————————————————

[i] http://biometrics.nist.gov/cs_links/face/mbe/MBE_2D_face_report_NISTIR_7709.pdf
Multiple Biometric Evaluation (2010) Report on Evaluation of 2D Still Image Face Recognition
Patrick J. Grother, George W. Quinn and P. Jonathon Phillips

[ii]http://allevate.com/blog/index.php/2012/07/17/advances-in-face-recognition-technology-and-its-application-in-airports/
Advances in Face Recognition Technology and its Application in Airports
Carl Gohringer,  Allevate Limited,
July 2012

[iii]http://www.ibia.org/download/datasets/929/Atick%2012-7-2011.pdf
Face Recognition in the Era of the Cloud and Social Media: Is it Time to Hit the Panic Button?
Dr. Joseph Atick
International Biometrics and Identification Association

[iv] http://www.piafproject.eu/

[v] http://www.biometricsinstitute.org/pages/privacy-charter.html
Privacy Charter
Biometrics Institute

[vi] http://www.iata.org/publications/Documents/2012-iata-global-passenger-survey-highlights.pdf
2012 IATA GLOBAL PASSENGER SURVEY HIGHLIGHTS
The International Air Transport Association (IATA)


“From grainy CCTV to a positive ID: Recognising the benefits of surveillance”

Interesting article in London’s Independent newspaper on CCTV surveillance and face biometrics.

Especially interesting is the view of the combination of biometrics over CCTV with artificial intelligence and behavioral recognition, as this does appear to be the way things are moving.

I agree that biometrics, and especially face recognition, can provide huge benefit to society. I also agree that there is a certain level of concern and distrust by large swathes of the population, some of it well-founded, and some of it based on misperception and incorrect knowledge.

In either case, I think it is dangerous to simply dismiss these concerns and objections simply because we feel “we know best”. I believe society can be much better off with the well placed and controlled use of this technology, but I also believe that we should be working with the civil liberties groups rather than fighting them. Ultimately, these systems need to be accepted if they are to succeed, and in order for this to happen, the public has to better understand the benefit to themselves, and have trust in the people using them.


SITA and NEC announce automated border control partnership

NEC Europe, leaders in biometric technology and SITA, the air transport IT specialist, announced an agreement to jointly provide an automated border control (ABC) gate solution. It incorporates sophisticated biometrics technology for use at immigration control points at airports in the European Union. The agreement comes as EU member states implement recommendations to move to self-service border control using ABC gates.

The speed and accuracy of this SITA/NEC automated border control gate helps speed up passenger flows at border control checkpoints while improving security and resource management. It incorporates face recognition, and optionally fingerprint verification, against e-passport data. Passengers can be processed through the SITA/NEC ABC gate in ten seconds or less

“SITA has significant experience in dealing with the challenges facing border control authorities around the globe and automated border control gates are recognized as a potential solution to the combined goals of improving the passenger journey and increasing border security,” said Dan Ebbinghaus, SITA Vice President, Government Solutions. “Working with NEC, our ABC gates combine SITA’s air transport industry experience and market knowledge with the fastest and most accurate face recognition software in the market. This combination will provide significant benefits to border control and airport authorities.”

ABC gates are less resource intensive as it only requires manual intervention by an immigration officer in rare cases when a match is unsuccessful. This frees up border security staff for other activities. In addition to the improved traveler experience, reduced waiting times can attract more airlines and increased revenue for the airport authority.

A core element in this ABC solution is NEC’s “NeoFace” face recognition algorithm which provides speed, accuracy and performance regardless of the database size and image quality. NEC face recognition technologies were ranked No. 1 in the MBE Still-Face Track in 2010 carried out by the National Institute of Standards and Technology (NIST), commissioned by the Department of Homeland Security.

Chris de Silva, Vice President IT Solutions, NEC, said: “NEC has a long history in innovation and with NeoFace we have extremely fast and accurate face recognition software, ideal for security applications. We have incorporated our software in a variety of security-based applications, but by integrating it into this new ABC gate, we believe it will significantly improve the efficiency of processing people through control checkpoints.”

He further added: “SITA has a wealth of experience as an IT integrator in the air transport industry and we are well-placed with our combined expertise to deliver a market-leading ABC solution across Europe.”


Article: Advances in Face Recognition Technology and its Application in Airports 1

You can download a PDF copy of this article by clicking this link.

The accuracy of face recognition has increased dramatically. The top performing algorithm in independent evaluations by the US National Institute of Standards and Technology (NIST) is now capable of providing reliable results in real-world environments; the technology is being deployed in airports today to enable everything from automated immigration processes, improved surveillance, security and seamless passenger travel, to the gathering of valuable statistical information pertaining to passenger movements.

1.0 The Business Environment

Airports are complex environments involving multiple stakeholders with conflicting requirements:

  • Government and border control.
  • Police.
  • Airport operators.
  • Airlines.
  • Retailers.

All parties must comply with all Government regulations and utilise the latest documents and passports from multiple issuing states while adhering to all security requirements.

1.1 More Passengers, Same Resources

Passenger numbers are relentlessly increasing; border crossings into the European Union by air alone are expected to increase to 720 million by 2030. The need to mitigate risk is constantly weighed against the requirement to ensure passenger mobility, whilst accurately and unambiguously identifying all those who move through this complex environment.

Biometrics is playing an ever-increasing role in response to these multi-faceted requirements.

2 Advances in Face Recognition Technology

Enter Face Recognition biometrics. This technology is set to transform CCTV surveillance. It is here. Ready to deploy. Now. A recent study by the US National Institute of Standards and Technology (NIST) demonstrated that the accuracy achieved by the top vendor can provide clear and measurable benefits to a range of applications, including surveillance.

2.1 Order of Magnitude Improvements between Subsequent Tests

Most remarkable is the rate of improvement in the accuracy of face recognition algorithms. NIST testing has demonstrated an order of magnitude of improvement in the False Reject Rates (FRR) every four years. Whilst maintaining a False Accept Rate (FAR) of 0.001, the FRR over 3 tests spanning 8 years were:

  • 2002: FRR of 0.2
  • 2006: FRR of 0.01
  • 2010: FRR of 0.003

Put simply, in the latest tests, if the best performing algorithm was set so that it would not falsely match two images of different people more than once in every 1,000 attempts, it would then fail to match two images of the same person only three times in every 1,000 attempts. In contrast, the 2002 tests mismatched 20 times for every 100 attempts.

FRR Results from NIST MBE2010

This arguably outperforms the accuracy of human beings.

2.2 Laboratory Testing versus Real World Environments

Although the above results are excellent, the controlled conditions of a laboratory environment are not representative of real-world conditions. They are a good indicator of results that may be attained when comparing photos of a similar quality taken under similar conditions (i.e. identifying 1 passport photo against a database of passport photos). However, photographs taken in an automatic border control e-gate or from a CCTV camera are not taken under the same control. These are commonly termed non-compliant captures.

Traditionally, face recognition software suffers degradation in accuracy when dealing with challenges such as variable lighting conditions or non-frontal images of the subject. Vendors that can better deal with these challenges deliver systems that perform in a consistently more reliable fashion in the field.

The latest NIST test indicates that the ability of the software to deal with the challenges of non-compliant photos has drastically increased. Face recognition software can now be reliably deployed in airport environments to deliver real and tangible business benefits.

2.3 Increased Tolerance to Angle / Pose

One way to predict how well a face recognition algorithm will perform in a real-world environment when dealing with non-compliant captures is to measure how well it performs in the laboratory with non-frontal photographs (where the subject‘s image is captured at an angle).

These lab results are an indicator as to which solutions will perform better when applying face recognition to CCTV cameras.

The recent NIST tests showed that the most accurate algorithm is highly tolerant to changes in pose. This indicates that detection rates from CCTV cameras should provide tangible benefits whilst minimising the level of false alarms.

Photos at a 25 Degree Angle

2.4 Increased Tolerance to Time Between Photographs

Additionally, it is often the case that the reference photographs we are comparing the live captures to are not recent. For example, most passports are valid for 10 years, so it is essential that we can still maintain a high level of accuracy when verifying photographs against older reference sets.

The NIST MBE 2010 study demonstrated that the highest performing algorithm was able to maintain accuracy rates that deliver quantifiable benefits in these circumstances.

Photos Taken 8 Years Apart

2.5 Lower Resolution Photos

It is also common for non-compliant face captures from CCTV cameras to involve photographs in which the subject’s face constitutes a small percentage of the overall frame of the picture or where the face resolution is not particularly high. This may be due to the use of a lower resolution camera or due to distance between the subject and the camera.

In December of 2011, NIST published another report entitled The Performance of Face Recognition Algorithms on Compressed Images. Although not the primary driver of this study, the results clearly show that the same top performing algorithm was able to generate the same high levels of accuracy with inter-eye distances all the way down to 24 pixels between the eyes, thereby providing another indicator of expected accuracy in real-world environments.

2.6 Real-World Results

There are numerous factors in a live deployment that need to be considered such as lighting, camera postion, distance of the subjects from the camera and the angles at which the sample photographs are taken.

Recently, a national government conducted an evaluation of an e-gate solution at an airport. As part of this evaluation, e-passport holders were invited to use the gates. A sufficient number of passengers were subsequently processed through the gates to provide proper statistical significance. Algorithms from three separate face recognition vendors were tested in the gate.

In this real-world scenario, passport photos of passengers were verified against a lesser quality livescan photo taken within the e-gate itself. The results were presented at the Biometrics 2010 Conference in London: the top performing vendor in the NIST test was able to achieve a real-world FRR rate of 1.1%. This is arguably a better result than can be obtained by a live border guard manually comparing passport photos against the passport holders.

FRR Attained in a Live immigration e-Gate

3 Current Applications of Face Recognition in Airports

Face recognition has evolved significantly over the past decade and has now attained a level of accuracy that provides real and quantifiable business benefit to all stakeholders in an airport environment. Solutions incorporating face recognition are already being deployed today.

3.1 Automated Border Control Gates at Immigration

Many nations world-wide have deployed e-Passports which are being carried by an ever-increasing percentage of the world’s population. This enables governments to deploy Automated Border Control (ABC) gates. In EU nations for example, these gates:

  • are for EU passport holders only.air travel
  • do not require pre-enrolment.
  • perform a 1:1 face verification of a live scan against the JPG on the passport chip.

In the UK these gates are being widely deployed at entry ports and seemingly form the backbone of the government’s strategy for automatically clearing EU passengers.

In Asia the three largest ABC deployments in the world (Singapore, Macau and Hong Kong) each process hundreds of thousands of passengers daily, maximising the efficiency of live border guards.

3.1.1 How it Works

The process involved in an ABC gate is fairly simple:

  1. The passenger approaches the gate and has their passport read by the e-gate.
  2. The validity of the data page on the passport is verified using a variety of tests.
  3. The information in the machine readable zone (MRZ) is verified against the data read off the chip.
  4. The passport information is sent to the appropriate government systems for the appropriate checks.
  5. If there are any problems thus far, the passenger is re-directed to a manned border lane, otherwise …
  6. A live photo is captured of the passenger (with appropriate liveness checks).
  7. Face recognition is used to verify the live capture with the photograph read off the passport’s chip.
  8. If the photo does not match, the passenger is assisted by a live border guard, otherwise…
  9. The passenger is allowed to proceed.

In this use of face recognition:

  • FAR represents the percentage of passengers holding a passport that does not belong to them that are wrongly admitted.
  • FRR represents the percentage of legitimate passengers who are wrongly re-directed to a live border guard due to the photographs not matching.

There have been no published studies of the FAR and FRR achieved by a live border guard, but it is generally accepted that face recognition operates at a higher level of accuracy, especially when a border guard has been on operational duty for more than 2 hours or has to deal with visual verification of multiple races of passengers. Most e-gate deployments in Europe today operate with an FRR of approximately 6% set against a corresponding FAR of 0.1%.

Recently, an officer responsible for a large deployment of e-gates in an international airport indicated that in his view, most imposters attempting fraudulent entry into the country prefer to try their luck with manned border guards rather than use automated gates.

3.1.2 The Business Benefit

You don’t have to look far today to read of the burgeoning deficits of most western nations. Austerity is the order of the day. Even in light of the expected year-on-year growth in passenger numbers, budgets are being cut. More and more often, improved efficiencies introduced by the sensible deployment of technology are being relied on to address these budget shortfalls.

Border guards are highly skilled and experienced staff deployed at the front-line of our nations defences. 99% of travellers entering a country are benign. Routine checking of travel documents and verification of valid ownership are tasks that can now be better performed by technology, thereby enabling the automated egress of legitimate travellers and allowing the border guards to focus on and find the 1% of the travellers they really want to speak with. In effect, removing the haystack to reveal the needle.

It is also relevant to note that the higher the accuracy of the face recognition solution deployed, the lower the FRR realised, thereby resulting in fewer passengers redirected to a live border guard and a lower cost of total ownership.

3.1.3 An Example

Another nation that has recently trialled the deployment of 4 ABC lanes determined the following:

  • Without the ABC lanes, 8 manual lanes required 8 border guards.
  • With the ABC lanes, the same 8 border guards were able to monitor 12 lanes.
  • Without the 4 ABC lanes, 8 border guards oversaw the entry of 950 passengers per hour.
  • With the 4 ABC lanes, 8 border guards oversaw the entry of 2,400 passengers per hour.

Even with the deployment of a limited number of ABC lanes a real and tangible benefit was realised.

3.2 Trusted Traveller Systems

Most ABC solutions deployed today take one of two forms:

  • Non-Registered, for holders of e-Passports from authorised countries (as discussed above).
  • Registered, for holders of passports from countries not authorised to use the Non-Registered lanes (or holders of older passports without a chip).

Examples of the latter include the US Global Entry, Dutch Privium (collectively FLUX) and the UK IRIS systems.

As non-registered systems become more commonplace and the number of e-passport holders continues to rise, the business case for governments to provide separate free-to-use Trusted Traveller systems becomes vague. Ideally, given the limited space available in airports, the best scenario involves these passengers using the same physical e-gates as users of the non-registered systems.

Existing e-gates can be modified to accommodate holders of e-Passports from other nations. An additional step in the process flow allows the e-gate to cross-reference against a database of pre-enrolled and vetted Trusted Travellers. An additional face verification can be performed against the stored face details of the enrolled passenger.

3.3 Departure and Boarding Gates

The previous example depicts the use of biometrics to facilitate passenger processing at immigration and to introduce efficiencies to the tasks of border control officials. Airport operators and airlines are also increasingly turning to biometrics to facilitate the flow of outbound passengers through airport terminals.

Simplifying Passenger Travel (SPT) was an initiative led by airlines, airports, governments and technology providers which proposed the “Ideal Process Flow”. The goal was to combine e-passports, biometrics and network infrastructure to enable the automatic identification and processing of passengers to move them through the airport seamlessly while freeing up staff to concentrate on security threats and customer service.

While the full ambition of assigning a single biometric identifier to a passenger’s entire airport journey, from booking, to check-in, bag drop through to security and eventually boarding is yet to be realised, key elements are already being implemented by airport operators.

3.3.1 The Problem

Many airport terminals have a single common departure lounge for both domestic and international passengers. Here exists the potential for a departing domestic traveller to swap boarding cards with an arriving international traveller, thereby enabling the arriving traveller to transit to a domestic airport and bypass immigration processes.

3.3.2 The Solution

This problem can be remedied by introducing automatic gates with face recognition at the entry to the common departure area and at the gate prior to airplane boarding. The automated gate at plane boarding captures the passenger’s face and verifies it with the face captured and associated with the boarding card when the passenger entered the departure area, thereby detecting if a boarding card has been swapped.

3.4 Surveillance: Real Time Watchlist Alerts

Matching faces captured from CCTV against photographic databases has long been the holy grail of face recognition. These systems are now being deployed today.

Although the results obtained in the NIST evaluations do not reflect the results that can be obtained in a live surveillance environment, it stands to reason that solutions that incorporate the best performing algorithms will also yield the highest accuracy results when matching CCTV images against a watchlist.

3.4.1 What it Delivers

These solutions are designed to integrate with existing surveillance processes; faces are extracted in real-time from the CCTV video feed and matched against a watchlist of individuals. When the system identifies an individual of interest, it raises an alert that can be responded to rapidly and effectively.

In this application of face recognition:

  • FAR represents the percentage of people captured by a CCTV camera that are falsely matched against the watchlist (in essence the number of false alarms raised by the system).
  • FRR represents the percentage of people captured by a CCTV camera who are in the watchlist but for which no alarm is raised.

The alerting mechanism is a binary process. If the system raises too many false alarms, it will quickly be ignored by those tasked with responding to these alerts. The objective of these systems is to minimise the false alerts to a manageable level, while detecting the highest possible percentage of people moving past the cameras who are in the watchlist (true ID rate).

3.4.2 Challenges

It is essential that expectations are set appropriately. Scenarios where thousands of cameras are scanning large crowds of people in day and night environments and from a distance to identify individuals of interest are still largely unrealistic. The best results are obtained:

  • Using newer high definition cameras (3-5 megapixels).
  • Indoors with uniform lighting or outside during daylight in the absence of specific glare.
  • Where people are generally facing the same direction and moving towards the camera.
  • In a suitable pinch-point, such as in a corridor, lane or access gate / turnstile (not large crowds of people).
  • Where cameras are positioned in such a manner as to minimise the angle to the face (ideally < 20 degrees).

Additionally, as the system is comparing poorer quality photos captured from CCTV, it is imperative that the highest quality reference photos are inserted into the watchlist. Systems comparing poor photos against poor photos operate at significantly reduced accuracy levels.

Even with the above considerations in mind, there exist substantial opportunities and environments in which these solutions may be deployed to deliver significant results.

3.4.3 Technical Considerations

These solutions are typically deployed in environments where large numbers of people may be crossing the cameras. As such, depending on the size of the watchlist, a very large number of face verifications need to take place. Such solutions potentially require intensive use of server infrastructure.

Typically, the main considerations that determine the server infrastructure required are:

  • The size of the watchlist.
    (Typically, these would only contain key or significant individuals.)
  • The number of people moving across the camera(s).
    (This represents the number of transactions or searches against the watchlist.)
  • The response time required in which to raise an alert.
  • The number of frames per second which are being captured by the cameras.
    (The higher the frame rate, the more times you capture the same person walking past the camera.)

Real-time searching of an entire criminal database is not typically feasible; consideration should be taken when determining who should be inserted into the watchlist to minimise its size. Typical watchlist sizes are in the hundreds or thousands.

The two major areas of processing inherent in such a system include:

  • Creating biometric templates of all the faces moving across the CCTV camera.
  • Matching these biometric templates against the watchlist.

Of these, template creation generally requires the most CPU power and time.

Therefore very careful consideration must be given to the number of frames per second (fps) the cameras are running at. Many systems typically run at 5-10 fps. While the processing power is significantly reduced, so is the overall accuracy of the system. The lower the fps, the more likely it is that the system will throw away frames containing a high quality image of the individuals’ faces.

To obtain optimal accuracy, cameras should be running at up to 20fps. However, this will result in more images of the same person being captured, resulting in a higher level of template creations and searches.
Solutions must be designed with scalability in mind, allowing the most efficient use of server power available.

3.4.4 An Example

An example of an existing live deployment in an airport environment consists of:

  • Up to 10 five megapixel cameras running at 25 fps.
  • A peak transaction rate of 1,000 people per minute moving across the cameras.
  • A watchlist of up to 1,000 people.
  • An alert response time of 5 seconds.

Each person is captured tens of times, resulting in tens of thousands of template creations per minute and tens of millions of biometric verifications per minute.

In this environment, assuming suitable environmental conditions and positioning of the cameras, this system identifies people in the watchlist up to 90% of the time (true id rate) with only one false alarm per day. If operators are willing to accept more false alarms, the true id rate can be increased by configuring system tuning parameters and lowering matching thresholds.

Such systems are already running today.

3.5 Surveillance: Forensic Video Analysis

The increase in the use of CCTV cameras has led to an ever increasing volume of archived video footage. The intelligence in this footage typically remains inaccessible unless appropriately analysed and indexed. Reducing investigation hours when limited resources are available is essential. Such systems can be used to populate databases of “seen” individuals, thereby enabling authorities to search for specific people of interest to determine if, when and where they have been present.

3.5.1 How it Works

  • Faces of individuals are captured from CCTV and archived in a database.
  • Authorities can search the archive using a photo to determine a camera ID and timestamp.
  • Playback of the relevant recording can be enabled by storing pointers into the video archive.

3.5.2 Usage Example: Passengers without Documentation

One usage already deployed today is to quickly and accurately determine the point of origin of arriving passengers without documentation, such as asylum seekers.

If a passenger presents themselves to immigration without documentation and does not provide accurate or complete information about themself, authorities can capture a photograph of the person and search the database of archived faces. If cameras are placed in aerobridges to record disembarking passengers, it is then a simple process to identify on which flight the passenger arrived.

3.6 Queue Management and Flow Analysis

It is becoming increasingly important for airlines and airport operators to monitor queue lengths and passenger flows within the airport. Understanding peak and quiet times is essential to enable sufficient and efficient staffing and resourcing. Raising alerts to manage unforeseen queues is critical for ensuring passenger satisfaction as well as for ensuring that all SLAs with other stakeholders, such as airlines or government agencies, are adhered to.

A common solution thus far has involved the tracking of bluetooth enabled devices such as PDAs and smartphones which are carried by passengers. However, relatively low percentages (approximately 15%) of passengers carry such a device, let alone have the bluetooth on the device activated.

A solution that provides a much more comprehensive data set and accurate information is needed.

3.6.1 The Application of Face Recognition

Solutions using CCTV with face recognition can timestamp when individuals are detected at known camera locations, thereby providing highly accurate information on passenger flows such as:

  • How long does it take to move between two or more points? (such as check-in to security)
  • What are the averages and when are the peaks?
  • How does this vary with time of day?

…as well as providing invaluable insight on how passengers move through the airport:

  • What percentage of passengers move from security to duty free?
  • How many of these are male / female?
  • How long does the average passenger spend shopping in duty free?
  • How is this impacted by queue lengths?

Importantly, no specific passenger identifying information is recorded.

3.6.2 How it Works

As passengers enter an area of interest they are acquired by a camera and anonymously enrolled into the system:

  • CCTV cameras enabled with biometric technology are installed at appropriate areas of interest.
  • Passengers are automatically searched against the database of enrolled individuals.
  • The passenger’s anonymous record is updated with a camera number and timestamp.
  • The database is automatically purged as required at regular pre-defined intervals.
  • The system can raise the appropriate alerts as required (i.e. queues too long).

4 Privacy Considerations

Any article on face recognition would be seriously remiss without at least mentioning privacy. There are a multitude of sources available for detailed discussions on privacy versus benefit of this technology, including the views of this article’s author; readers should familiarise themselves with this issue before considering any deployment of face recognition.

5 What’s Next?

As the use of face recognition continues to be substantiated, more ingenuitive applications will be deployed. Cloud-based services will enable the transfer of expensive computing power out of the airport into shared server facilities. Face recognition will assign a passenger a single unique and transient identity during their movement through the airport, thereby allowing them to be processed by multiple applications seamlessly and effortlessly. Passenger movement through an airport environment can be tracked up to the point of their departure. Personalised way-finding solutions can guide individual passengers to their specific gate, thereby reducing flight delays and passengers who are delaying flights can be quickly and easily located.

6 Summary

The accuracy of face recognition has increased dramatically over the past years. The top performing algorithm in independent evaluations by the US National Institute of Standards and Technology is now capable of providing reliable results in real-world environments and the technology is being deployed today in airports to enable everything from automated immigration processes, improved surveillance and security, seamless passenger travel and the gathering of valuable statistical information pertaining to passenger movements. The number of potential applications of this technology will continue to deliver benefits in creative ways we have yet to imagine.

The business benefit is real and quantifiable.

7 About the Author

Carl is the founder of Allevate Limited (http://allevate.com), a consultancy focused on providing strategic expertise for identity projects that incorporate biometrics, automation and analytic technologies. With over 20 years’ experience working in the hi-technology and software industry, he has spent the past 10 years enabling the deployment of biometric technologies to national infrastructure projects. Carl started working with biometrics whilst employed by NEC in the UK. Allevate continues to work closely with NEC on identification projects in Europe for government, border control and law enforcement.

You can download a PDF copy of this article by clicking this link.

This is the author’s original version of a work that was accepted for publication in Biometrics Technology Today (BTT). Changes resulting from BTT’s publishing process are not reflected in this original version, and as such this article may differ from the version subsequently published in Biometrics Technology Today, VOL: 2012, ISSUE: 7, Date: July, 2012,  DOI: http://dx.doi.org/10.1016/S0969-4765(12)70148-0


Using Face Recognition to Monitor Queues and Passenger Flows in Airports 2

The Business Environment

It is becoming increasingly important for airlines and airport operators to monitor queue lengths and passenger flows within the airport. Airport operators have invested significant time and money on investigating technologies that can provide useful metrics.

Understanding your peak and quiet times is essential to enable sufficient and efficient staffing and resourcing. Raising of alerts when unforeseen queues arise is critical for ensuring passenger satisfaction, as well as for ensuring that all SLAs with other stakeholders, such as airlines or government agencies, are adhered to.

Thus far, a common solution has enabled the tracking of bluetooth enabled devices, such as PDAs and smartphones, which are carried by passengers. The obvious drawback is that only a relatively low percentage of passengers will carry such devices, let alone have the bluetooth on the device activated.

However, even a penetration rate of 10-15% can provide a large enough sample to give statistical significance. Even so, a solution that provides a much more comprehensive data set and accurate information is needed.

The Application of Face Recognition

Using CCTV integrated with face recognition biometrics enables a solution that timestamps when individuals are detected at known camera locations, thereby providing highly accurate information on passenger flow information, such as average and peak queue times:

  • How long on average does it take to go from Checkin to Security?
  • How does this very with time of day?
  • When are the peaks?

.. as well as providing invaluable insight on how passengers move through the airport:

  • What percentage of passengers move from security to duty free?
  • How many of these are male / female?
  • How long does the average passenger spend shopping?
  • How is this impacted by queue lengths?

Importantly, no specific passenger identifying information need be recorded, and data can be purged at regular intervals.

Airports, such as London City, are already deploying such technology.

How does it work?

As passengers enter an area of interest and are acquired by a camera, they are automatically enrolled into the system:

  • CCTV cameras enabled with biometric technology are installed at appropriate areas of interest.
  • Passengers are automatically searched against the database of enrolled individuals.
  • The passenger’s record is updated with a camera number and timestamp.
  • The data is automatically aggregated to provide real-time analysis of passenger flows and movements.
  • The database is automatically purged as required at regular intervals. (ie overnight)

Features

Using face recognition for such an application can provide many tangible features, including:

  • Aggregated passenger flow data.
  • Average time to move between two or more points.
  • Average time staying in a specific area.
  • Real-time reporting information.
  • Reporting over specific time frames.
  • Historical data comparison.
  • Alerting mechanism (ie, queues too long)

Benefits

  • Does not capture passenger personal details.
  • Passenger data is purged regularly.
  • There are no data protection issues.
  • Unobtrusive and requires no passenger interaction.
  • Does not require special devices, such as Bluetooth phones.
  • High sample set and penetration rate.

To Sum

Airports are complex environments involving multiple stakeholders, often with conflicting requirements. Their efficient operation requires real-time and reliable operational data. It was only a matter of time before operators turned to advanced technologies such as face recognition in order to provide such measurable and quantifiable date.

Clearly, the more accurate the technology, the more reliable the data on which the operator is basing critical business decisions. Independent studies by NIST clearly indicate that face recognition is now operating at a level of accuracy to enable such decision making.

The quality of the aggregated data provided by face recognition by far surpasses that of traditional application of technologies to this problem, such as bluetooth monitoring.


In the wake of the London riots, is the privacy versus security debate now all but dead?

Allevate Presenting at Biometrics 2011

Synopsis

Recent advances in the accuracy of face recognition are resulting in an explosion of its use, coupled with increasingly vociferous cries from privacy advocates. The benefits from the uses of this technology are clear. But does it enable even further and easier harvesting of private information about us as individuals, without our knowledge or consent? This presentation does not attempt to analyse the adherence of face recognition to the nuances of privacy legislation. Rather, it explores the emerging trends in the application of face recognition, from law enforcement and security / surveillance, through to commercial applications, to enable each of us to form our own views on where the boundary between face recognition and privacy lies.

Article: Face Recognition: Improved Benefit? Or Erosion of Privacy?


Face Recognition: Improved Benefit? Or Erosion of Privacy? 6

[polldaddy poll=5701230]air travel

A Surveillance Society?

I’m sat in Heathrow waiting for an early morning departure for a business trip. Sipping my coffee, I look casually around trying to spot the cameras. They’re cleverly hidden. Am I being watched? Doubtful. Am I being recorded? Almost certainly.

This is a daily fact of life for most Londoners. It’s widely known that our city is one of the most heavily recorded in the world; a fact that is consistently debated and often criticized. Yet for all the discussion, the fact remains. We don’t like it, but we accept it. Why? Personally, my true dislike is more of the necessity of this fact rather than the fact itself.

Carol Midgley wrote an excellent opinion piece (The Times, Sat 27th August, 2011) entitled “I’ll pick Big Brother over a hoody every time”. I recommend a read. Though clearly biased, and seemingly designed to stoke the debate with anti-CCTV campaigners, her conclusion was simple: In the wake of the London riots, the privacy-versus-necessity debate of CCTV is now all but dead. Do I agree? Let me come back to this.

Face Recognition and CCTV

Enter Biometrics. Face recognition technology to be precise. This technology, along with the wider field of video analytics, is set to transform CCTV surveillance. Video analytics is arguably a nascent technology, but face recognition on the other hand is here. Ready to deploy. Now. A recent study by the US National Institute of Standards and Technology (NIST) demonstrated that the accuracy achieved by the first place vendor (NEC) can provide clear and measurable benefits to a range of applications, including surveillance.

It seems that every new technology brings a realisation of new benefits and efficiencies, countered by a plethora of malicious uses of the technology by the less desirable elements of our global society, quickly followed by counter-measures and protections. This is a saga that we are all already familiar with in our daily lives. Examples range from the severe and extreme of nuclear medicine versus atomic weapons, through to online credit-card shopping versus financial identity theft. I’ve recently had a credit card used for over £3,500 of illegal transactions. Though this incident was highly inconvenient and disruptive to my life, I did not hesitate to accept a replacement card. Not to do so would have unacceptably disenfranchised me from modern society.

Back to face recognition. It hasn’t taken long for business minded technology companies to devise a whole range of new uses of this technology, all focussed on delivering bottom line business benefit. Almost as quickly arrive the cries of the privacy advocates. I’ve been reading with interest the sudden explosion in main stream news over the past few months highlighting new uses of face recognition, while very carefully considering the concerns vociferously raised by the technology’s opponents. A key fact often cited is that the technology is not 100% accurate. Even an excellent identification rate of 97% can produce a significant number of false identifications and / or missed identifications in a large sample population.

Let’s take a look at some examples.

Public Safety and Policing

While sat here in the terminal waiting for my flight, I’ve already grudgingly accepted that images of me sipping my coffee are almost undoubtedly being recorded. I may not be aware, however, that when I passed through security my photograph was taken. This wasn’t immediately obvious or openly advertised, but it happened. Shortly, my photograph will be taken again when I board my aircraft and compared to the photograph taken at security. International and domestic passengers share a common departure area, and this is done to ensure boarding cards aren’t swapped, thereby potentially enabling an international passenger to transit through to a domestic airport and bypass immigration controls. On a 1:1 verification, false matches are very low. If I’m a legitimate passenger, my concern is that the two photographs do not match, for which the worst case scenario is inconvenience.

Perhaps the borders agency is also comparing my photograph against a known watchlist of suspect individuals. This nature of deployment is usually used to enhance existing procedures, and not replace them. The system will provide increased security, in turn further protecting my safety while flying. I’m OK with this. Of course, there is also the prospect of misidentifying benign travellers. Though unavoidable, as long as the number of false matches are kept sufficiently low to ensure the cost of dealing with these exceptions doesn’t obliterate the benefit realised from the system, it can be argued that the greater good justifies the inconvenience faced by the occasional innocent passenger while their true identity is verified.

Upon my arrival at my destination, I may very well be offered the opportunity to use my new e-passport to speed through immigration at one of the many shiny automatic e-Gates springing into operation. In the early stages these definitely were a great benefit, allowing me to march past the long queues of travellers and expedite my passage through the airport. No complaint from me. As long as false matches are lower than what is achieved by a live border guard (which many studies suggest they are), then security should be improved. And false matches only apply to illegal passengers travelling on a false or stolen passport. Exceptions generated by valid travellers who do not match with their passport will generate some inconvenience by necessitating they speak to a live border guard. As e-gates become more commonplace, I predict I’ll just be queuing in front of an automatic barrier instead of a manned immigration booth. However, the efficiencies achieved should enable the border guards to concentrate on more intelligence-led activities, rather than simple rote inspection of passports, thereby increasing security and putting my taxes to more efficient use.

As I move through the airport, or for that matter in any public location such as a stadium or railway station, law enforcement authorities may be using my captured image to search against a database of suspects. Does this trouble me? Let’s look at a couple of scenarios.

I’m already being recorded. If I were to commit a crime, then it is likely that the video would be retrieved and officers would try to identify me. This is already happening and I doubt anybody would argue that this is an invasion of privacy. If face recognition technology can assist them with this arduous and tedious task, perhaps by automatically trying to match my face against databases of known offenders, and saving countless hours of police time, I’m all for it. Too bad for the criminal.

(I was incensed by the meaningless violence and destruction demonstrated during the recent riots in London. Newspaper reports have indicated that the UK’s police will be examining CCTV footage for years to come in their efforts to bring the perpetrators to justice. I am absolutely in favour of anything that can be done to expedite this process and save police time.)

But as a law-abiding citizen carrying on with my own business, how do I feel about having my face automatically captured and compared against a watchlist database of “individuals of interest”? There is potential to cause disruption to an individual’s life or place them under undue suspicion if they are falsely identified. That my face is being actively processed rather than just recorded gives more cause to pause and consider.

Having done this, I am prepared to accept this use case, if the technology is operating at a sufficient level of accuracy to ensure that the chances of being misidentified while conducting my daily activities remains low. I also expect the technology to be deployed wisely in situations where there is demonstrable benefit to public safety, such as at transport hubs, large gatherings, public events or areas of critical national infrastructure.

Most people already accept that the reality of the world today necessitates certain infringements on our liberties. The introduction of technology is a key tool in the fight against crime. No system is perfect, and the potential for an undesirable outcome of a system should not always result in the abolishment of that system. Few would argue, for example, to abolish our judicial systems and close our prisons to eliminate the possibility of a miscarriage of justice. Similarly, the benefits to public safety from face recognition are too great to ignore, though we must continuously strive to minimise the false identifications.

I agree with Ms. Midgley on this one.

Commercial Applications

Most criticism that I have been reading in the press in the past view months appears to be levelled at the widening application of face recognition in business related or commercial applications, not with public safety.

My flight is about to board, so let’s continue my journey through the terminal. As I saunter to my gate, my attention is caught by an impressive advertising display; a multi-plasma video wall. It was the amazing technology that caught my attention rather than the advert itself. Just as I’m about to glance away, the sunlit beach and blue ocean depicting the under 30’s surfing holiday fades away, to be replaced by a two-for-one spectacle offer, followed by a distinguished gentleman telling me how easy it was for him to “wash that grey away”.

As I self-consciously stroke the hair at my temples, I wonder: Was this a mere co-incidence? Multiple vendors delivering solutions for advertising have announced technology that can count the number of people watching an advert at any given time, and even estimate their age, dwell time, sex and race. While providing invaluable information for the advertiser, it can also allow them to dynamically change the adverts in real time to more appropriately target the demographic of the current viewer(s). Recent reports in the Los Angeles Times (21st August 2011) suggests that this is already widely deployed in Japan, and is being considered by the likes of Adidas and Kraft in the UK and the US.

While this is not technically face recognition, it is still worth noting as much of what I have been reading has been lumping the two technologies together. The key consideration here is that this form of technology is not actually identifying anybody, or extracting personally identifiable information. This doesn’t bother me in the least. Businesses have always tried to use whatever edge they can to more tightly tailor their message to their customer’s specific needs and wants. It may even benefit me by alerting me to more relevant products or services.

What if, on the other hand, the advertiser had negotiated an arrangement with another organisation, for example a social networking site such as Facebook. If they supplied them with an image of my face, along with information on which portion of the advert caught my attention, Facebook might be able to identify me from its database of photographs, enabling them to harvest valuable information about me. While I can see this would present a huge commercial advantage to them, and whomever they chose to sell this information on to, I can only hope that the commercial damage from the backlash of incensed users would outweigh the gain.

If I have some leisure time while on my business trip, there will doubtlessly be many activities at my destination to occupy me. I may have a quiet drink in a bar, or perhaps take a punt at the tables in the local casino. And yes, face recognition technology is being used even in these places. It’s been reported that bars and clubs are using gender and age distinguishing cameras to count people in and out, and make this information available over mobile phone apps. The youth of today can now determine before they set out which establishment holds their best chance of success. While I am well beyond having any use for this particular application, I can see how this may catch on in certain demographics of society. Any reputable establishment should clearly display such technology is in use and should make no attempt to harvest or make available any personally identifying information. Are all establishments reputable?

More concerning to me is the increasing use of face recognition by social network sites. Both Google and Facebook are actively exploring uses. Automatic tagging of photographs being uploaded to Facebook is already occurring. Being inadvertently photographed while on my business trip and automatically tagged when the photographer uploads it does not appeal to me, no matter how innocuous my activities at the time may happen to be.

Recent studies published by Carnegie Melon University demonstrating the potential to use large databases of photographs on social networking sites to glean confidential information should also be a cause for concern. The younger generation of today appear more and more willing to share intimate and private details online, without any thought (in my view) of the longer term or wider ramifications of doing so. This is an issue that is much larger than face recognition, but I can understand the worry that face recognition can help to tie it all together.

Improved Benefit or Erosion of Privacy?

When I first entered the biometrics field, I was attracted by the “neatness” factor of the technology, and of the potential for it to deliver benefits to society. I have to admit I paid scant attention to privacy concerns. Over time, as the voices of privacy advocates grew louder and more numerous, I started to listen and then to actively seek out their opinions. I am still a firm believer in this amazing technology, and endeavour to play an active role in its application for the positive transformation of society. However, I am grateful for the messages and insight provided by these campaigners; they have definitely transformed my thinking, and have made me consider much more carefully the application of biometrics.

From a law-enforcement and public safety viewpoint, face recognition holds great potential to increase the security of our society. By its very nature, our government holds power over us and our society, which is why it is our responsibility to choose our governments carefully. We have no choice but to hold a certain level of trust and faith in our law-enforcement organisations. Our society today contains more checks and balances than ever before, and our politicians our more in-tune with and responsive to the public mood. If this faith breaks down, then so does society.

In commercial applications, I also believe there is the potential for significant benefit to be realised from face recognition to both the consumer and businesses, but I am more concerned about the potential for abuse. To a certain level, the market will decide if the application of the technology is appropriate or not. Ventures people don’t like will fail. However we cannot always rely on market forces, and it is our collective responsibility to speak out when the need arises. Though it often lags behind, over time legislation keeps up with the advancement of technology. As our society changes with technical innovation, so too will the rules we collectively decide to govern our society. We will settle into an equilibrium reflecting the needs and views of all. But there will be a learning curve, and we will make mistakes along the way. That’s how society works.

So, does face recognition represent an improved benefit, or an erosion of privacy? I suggest it has the potential to be both. It is everybody’s responsibility to ensure the benefit is worth the price paid. I absolutely believe we must have both the proponents of this technology and the advocators of privacy; we all have a role to play to decide how face recognition will be applied over time.

The abolishment of either the technology or the voices of those monitoring its use and advocating our privacy would be to the detriment of society.

Final Thought

Just before I board my flight, let me leave you with this final thought. Imagine for a moment that a loved one of yours has come to harm. The authorities can use face recognition to aide in their recovery, and / or to ensure that justice is done. Are you concerned with privacy?