CCTV


Article: Helping to Counter the Terrorist Threat using Face Recognition

Forensic Media Analysis Integrated with Live Surveillance Matching

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

 

Against the backdrop of budget constraints, threats from terrorism, organised crime and public disorder continue to rise. Authorities can remain resilient through the targeted application of technology. Advances in face recognition coupled with the mass availability of digital media and continuously cheaper computing provides unique opportunities to enhance the efficiency of forensic investigations to enhance public safety. Processing of digital media can be automated in a virtualised and elastic computing environment to identify and extract actionable intelligence. Processing is scalable, continuous, consistent and predictable. Analysts can focus on investigating and confirming suggested results rather than watching countless hours of media in the hope of stumbling across intelligence. Such a centralised platform can also be used to search in near real-time faces from any number of remote cameras against centralised watchlists of individuals of interest.

1. A Need for Enhanced Safety and Operational Efficiency

Risks are increasing. Recent events demonstrate that the threat landscape is substantial and becoming more fragmented, consisting of a greater number of smaller and less sophisticated plots. The targeted application of technology can play a key role in improving the efficiency of our police and intelligence agencies and maintaining readiness to both disrupt and respond to major events.

2. A Relentless Increase in Digital Media

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

  • Digital Forensics (confiscated phones, computers, flash drives etc).
  • Open Source Intelligence (Internet and Dark Web).
  • Crowd- sourced from members of the public (HD cameras on mobile phones are ubiquitous).
  • Police Body Worn Video.

When tragic events or social disorder occur, investigators have a long and arduous task of reviewing countless hours of media, 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 scrutinywill deliver concise and consistent information in a fraction of the time taken by operators undertaking the task manually.

3. An Automated Media Processing and Exploitation Solution

Police, intelligence and other public order agencies can benefit from the application of a powerful media processing solution designed to ingest, analyse and index, in an automated fashion, very large quantities of media from multiple sources to transform them into usable assets. Utilising virtualised and elastic computing environments enables the platform to be rapidly scaled up and down in response to unfolding events.

Once processed, 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.
  • 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) to enhance investigations and association of data.
  • Integration into existing system environments, databases and components.

3.1  Incorporating Other Detection Capabilities

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

  • Biographic filtering and Fuzzy Match capability.
  • Automatic Number Plate Recognition. (ANPR)
  • Voice Biometrics.
  • Object / Logo Recognition.

Vendor independence allows the use best-of-breed algorithms. Newer and better algorithms (COTS and GOTS) can be plugged in without having to replace the entire platform.

3.2  Working with Geo-Location Data

An increasing amount of media is captured on devices affixed with location determining technology. Often, this geo-location data is incorporated into the media metadata, thereby providing potential to further enhance the analysis of media. Geo-location can be used to:

  • Compartmentalise and refine analysis by location of media creation.
  • 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.

3.3  Architecture and Integration with Existing Systems

In addition to utilising COTS components, open standards and cloud-computing architecture 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.
  • Dynamic alteration and sharing of watchlists, media, system-generated results and operator analysis.

3.4  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 public sector hosting offerings.
  • Fully self-host the solution on private and secure premises and datacentres.
  • Deploy in a hybrid manner.

Indeed, managed AWS or Azure offerings can be utilised to bulk process media, utilising non-return gateways to propagate identified sensitive data to more secure facilities.

3.5  Working Hand-in-Glove with Trained Forensic Investigators

Humans will always remain the critical and essential part of intelligence analysis; such solutions do not replace the intricate skills and knowledge of trained investigators. Rather, the operator is enabled to intelligently direct and apply their 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.

 

4 Potential Use Cases

There are multiple applications of a solution as described herein within military, law enforcement, intelligence and public-site security agencies. These are summarised into four broad categories:

4.1  Time Critical Investigations, Media of Critical Importance

Often, authorities need to quickly process evidence to identify and apprehend individuals. The scale of the investigation can be huge and the amount of media that needs to be processed massive.

The media acquired in these instances can be of such critical importance that the authorities may choose to review it all in its entirety. However, immediate and decisive action is critical. Rather than sifting through the media in a random or sequential fashion, a media analysis solution can quickly direct the investigators to portions of the media that are most likely to deliver immediate results. Full review of the media can be conducted afterwards.

4.2  Bulk Ingestion of Media Arising from Criminal Investigations

During routine operations or investigations, authorities may recover significant quantities of media from multiple sources that need to be processed to further the investigation or to assist in building an evidence base for prosecution. Examples include:

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

Automating processing provides 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. Intelligence in this media may be missed entirely and never acted upon.

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

  • Routine and automated processing of accessible media can flag actionable intelligence that may help disrupt future attacks.

4.4  Near Real-Time Watchlist Checking from Live Surveillance Cameras

By integrating any number of remote surveillance cameras to such a centralised matching platform eliminates the need to install and maintain costly local software and hardware to perform local face matching as well as the need to store potentially secure watchlist data locally at the camera locations. The problems associated with live streaming of HD media over low bandwidth network connections is resolved through the application of local face-detection and cropping; only small image files of cropped faces need be sent to the central data centre over encrypted channels.

4.4.1        Centralised Archive of “Seen Faces”

In addition to submitting search probes to the server for searching against one or more watchlists, search probes can be enrolled in a “seen faces” archive which can be interactively or automatically searched (using face recognition) by investigators or when submitting videos for processing.

 

5 A Compelling Business Case

The solution can be made available using a compelling SaaS model. The open and standard nature of the solution ensures it can run in existing on-premise datacentres or outsourced to secure hosting partners.

Whilst the human operator is an essential part of intelligence analysis, an entry-level system 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 in a more focussed manner 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 Summary

Security concerns are increasing whilst budgets are limited. The focussed application of technology can improve efficiency and aid law enforcement agencies to rise to this challenge.  The massive increase in the creation of digital media and the availability of cheap computing 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 to 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. The same centralised platform can also be used to search in near real-time faces from any number of remote cameras against centralised watchlists of individuals of interest.


On the BBC Newsnight Report on Terrorism and Face Recognition

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


Allevate Announces Continued Availability of MXSERVER on UK Crown Commercial Service Digital Marketplace (G-Cloud)

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 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 23 May 2017:  Allevate today announces the continued availability of MXSERVER on the UK Crown Commercial Service’s Digital Marketplace G-Cloud 9 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.

The below is extracted from our announcement of availability on G-Cloud 8.

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.


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.”

 


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 in the Securing Crowded Places Immersive Demonstrator at UK Security Expo

Allevate is pleased to participating with Sungard Availability Services in the Securing Crowed Places Immersive Demonstrator at the UK Security Expo on the 30th November and 1st December 2016 at Olympia, London.

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

Using the event venue itself as the place to be protected, the aim is to provide an integrated experience in which visitors are able to see innovative technologies and techniques in operation.  This will provide a realistic context as well as allowing discussion with Government and industry experts.

Allevate will be demonstrating real-world uses of Tygart’s MXSERVER to deploy watchlist detection using face recognition, and the application of face recognition to aide the post-event forensic analysis.

Allevate will be participating with our partners, Sungard Availability Services, Physical Tracking Systems, and Facewatchsungard

Additionally, Allevate’s founder Carl Gohringer will be providing a live demonstration of MXSERVER in the Technology Workshops and Live Demonstrations 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.

We look forward to seeing you at the exhibition on stand A41 in collaboration with Sungard Availability Services.


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.

 

 

 


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


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.


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.

 


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.

 

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[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.


Man Films People in Public. Interesting Statement on Surveillance and Privacy 2

A very interesting piece to spark debate regarding safety versus privacy.

WARNING: There are one or two minor instances of less than desirable language, mainly due to the state of annoyance of those being videoed.

I do not believe (though I’m not a legal expert) that the person filming did anything illegal, yet people clearly took offence at his actions. The point the cameraman is obviously trying to make is why then do people so willingly accept being recorded by surveillance cameras?

I think the main point this film misses, in my opinion, is that people do not understand the purpose or intent of the cameraman’s actions, and they then assume malfeasance, which then understandably provokes a negative response.

In contrast, for the most part, most people understand the intent and purpose of a surveillance camera in a public place (such as a store or train station): to protect public safety.

The main lesson to be learnt from this (in my opinion) is the importance of education and awareness, and ensuring your users / key stakeholders are aware of proceedings and bought into the concept from the outset.

Thoughts or comments?