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.

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


“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:

… and read more about the proposition at:

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).

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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.”

Does turning off the Iris system at Manchester and Birmingham represent a failure of biometrics? 2

News that the Iris biometric gates at Manchester and Birmingham airports have been turned off has been widely reported. (BBC: Eye scanners at England airports turned off, Register: Two UK airports scrap IRIS eye-scanners)

The comments that this represents a failure of biometric systems started to fly almost immediately.air travel

  • “Multi-million pound eye scanners, billed as a key tool in securing Britain’s borders, have been scrapped.”
  • “…the technology has been beset by problems,…”

… are typical of the comments and headlines making their rounds.

I admit the gates were not perfect and did require some getting used to in order to navigate your way through quickly.

But I think the systems were far from a failure, and the reality is a little bit more subtle than the headlines may suggest.

Let’s not forget the system was originally introduced in 2004, initially as a pilot.  At this time, such use of Iris technology was fairly innovative.  That the footprint of the pilot was gradually extended and became a permanent system is indicative that the system was fairly well received. The fact that over 380,000 people have voluntarily enrolled (myself included) makes it difficult to argue that the system is derided.

In my opinion, the turning off of the system at these two locations is more in line with a planned phasing out of this particular solution, for some rather more mundane reasons:

  1. The system  no longer fits border-automation strategy in the UK  moving forward. It has largely been replaced by the momentum to accommodate EU e-Passports holders,whose passports hold an electronic copy of their face photographic.
  2. As innovative as the technology was in 2004, it is now woefully out-of-date. Iris technology has moved on leaps-and-bounds in the 8 years since (as demonstrated by the Iris-at-a-distance  e-gate solutions for departing passengers at Gatwick airport). The initial investment undoubtedly has long since been written off, and the technology needs a refresh.
  3. The initial deployment was meant to be limited, and the contract has undoubtedly been extended numerous times. A complete and expensive technology refresh (as is required) without an open and competitive re-tender would undoubtedly not rest on firm legal ground.
  4. The business model was never well thought out. It is completely funded by the UK government and can be used by any nationality completely free of charge.

This Iris system is intended for pre-registered Trusted Travellers, who are pre-vetted before they can use the system. At point of use, it is a 1:n Iris check and no travel documents are required.

Since the system has been deployed, most European Union (EU) nations have deployed e-Passports and an ever-increasing percentage of the EU population is now carrying a chip passport. The Iris gates have been gradually been superseded by a new breed of e-Gates that:

  • are for EU passport holders only.
  • do not require pre-enrolment.
  • perform a 1:1 face check against the JPG on the passport chip.

These gates are now being widely deployed at UK ports of entry and seemingly form the backbone of the government’s strategy for automated passenger clearance. This is only natural, as by far the bulk of passengers entering the UK are EU citizens.

If the remaining Iris gates are end-of-life’d, this will clearly leave a hole in the border automation strategy, mainly those passengers that:

  • are not EU citizens.
  • are EU citizens but do not yet have an e-passport.

Arguably, the second of the two will become less of a problem as time passes, as holders of older passports have their passports renewed.

The former, however, will form a minority of arriving passengers, and the business case for the government to provide a free-to-use Trusted Traveler system remains vague. More likely than not, any replacement system  will take the form of a paid subscription requiring a pre-enrollment with vetting.

Ideally, given the limited space available airports, the best scenario would involve these passengers using the same physical e-gates as EU passport holders.

In my view, allowing these systems to reach their end-of-life is not an argument for the failure of biometrics deployed at the border. The fact that a system that was only ever meant to have a limited deployment lasted this long and was only replaced by a government strategy that is more harmonised across EU nations, is a testament to the value this technology provides.

Thank you project IRIS, but I won’t miss you. I use the new e-Passport e-Gates now.

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)


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)


  • 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.