Security Facial recognition is a hot topic and there has been considerable media interest in the development and use of this technology.  Many companies are banking on it as a technological force that can solve complex problems and shape and improve our day-to-day lives.

At the same time, the technology is highly controversial, with privacy, accuracy and its ethical use raising key concerns.  Is facial recognition as inaccurate and intrusive as the media would have us believe?

Media Reporting on Facial Recognition

Facial recognition has received a lot of bad publicity recently and media reports have highlighted concerns over the invasion of privacy and the constant monitoring of our daily lives.

Privacy advocates have deemed the technology ‘unlawful’, ‘dangerously inaccurate’, and claim ‘fail rates of 92%’. These reports are extremely misleading, but headlines of this nature tend to resonate and as such, there is a broadly negative perception of facial recognition with the public.

These reports are mainly based on previous evaluations of the technology going back a few years. False match figures reported to be 90% and above are largely taken out of context and highlight misunderstandings on how test data has been presented and therefore are not a reflection of its true accuracy.

However, the technology has now reached a level of maturity and accuracy allowing it to be deployed and integrated across a wide range of critical applications and sectors. In fact, there are vendors who can verify accuracy rates of 98% and above with false match rates (based on a 1:1 comparison) of 1 in a billion.

Facial Recognition and Optimisation of CCTV

According to the British Security Industry Authority (BSIA) there is 1 CCTV camera for every 14 people in the UK. London has the second largest CCTV network (second only to Beijing) in the world and each camera has the capacity to record and store 25 pictures every second. In contrast, facial recognition only compares a single picture against a database or watchlist of pre-validated individuals.

The facial recognition system will only send an alert to the CCTV operator if a subject of interest is positively identified from the watch list. The operator can then make the final confirmation of recognition.

This can also be used to review post-event footage from one or more cameras against pre-populated watchlists. This provides a semi-automated tool for security staff to identity persons of interest in a more focused, positive and less time-consuming manner.

Over the years police super-recognisers have been successful in identifying wanted persons. Facial recognition can retrieve data in seconds so can assist them by eradicating the need to review hours of pre-recorded footage, enabling them to make speedier identifications.

If we approach this technology as a way of integrating with personnel, the technology becomes an enabler of more informed human decisions, adds another layer of authentication and retains a healthy balance between machines and people.

There is a vast array of facial recognition products in the marketplace with widely varying levels of accuracy.  Therefore, rigorous evaluation is key when selecting a system. Not all algorithms perform the same, but the best algorithms are far ahead of the pack.  Commonly reported inaccuracies of the facial software are often down to poor installation of CCTV systems.

Correct camera installation is critical to ensure the optimum accuracy. Camera focus, height, angles, image database quality and lighting are all factors that need to be addressed.

Racial Bias

Facial appearance varies, both in terms of skin tone, cranio-facial structure and size. There has recently been speculation in the media around racial bias in facial recognition algorithms. For humans, the word ‘bias’ suggests an inclination or prejudice against a person, but the technology itself cannot behave in such a way. The bias can arise from insufficient ethnically diverse data being used to train the system. Therefore, when evaluating the technology, end-users should question and verify data sets used by the manufacturers to ensure accuracy across all ethnicities.

Benchmarking and NIST

The National Institute of Standards and Technology (NIST) is a science laboratory and agency within the US Department of Commerce. Its mission is to promote innovation and industrial competitiveness. Its rankings are viewed as the gold standard within the industry and a key benchmark for this technology.

From as early as 2000 they have conducted a variety of independent Facial Recognition Vendor assessments across a broad range of large data sets and applications. The test data is unknown to the participating vendors, thus enabling a level playing field that ensures algorithms cannot be ‘tweaked’ to perform favourably against a certain data set. The accuracy of these algorithms is then published by NIST approximately twice a year.

It is only recently that these technologies have undergone unprecedented improvements at an ever-quickening pace. By incorporating highly complex neural networks, developers have bolstered the ability of facial recognition algorithms to detect identities even when poor quality images are employed. The NIST report in November 2018 concluded that the entire industry has improved not just incrementally, but ‘massively’. It showed that at least 28 developers’ algorithms now outperform the most accurate algorithm from late 2013, and just 0.2% of all searches by all algorithms tested failed in 2018, compared with a 4% failure rate in 2014 and 5% rate in 2010.

Continual investment, research and development into this technology mean it is continuing to improve, and according to NIST, facial recognition is now 20 times better than it was just a few years ago.

Privacy and GDPR

Many manufacturers claim to be privacy and GDPR-compliant, but ultimately it is the end user’s responsibility to ensure the system is used in an ethical and GDPR-compliant environment. Completion of a data privacy impact assessment (DPIA) should be completed prior to its deployment. The DPIA will ensure that a validated watchlist has an inclusion threshold criteria and is regularly reviewed so additions and deletions are signed off at the highest level. This document outlines a strict code of conduct and procedural safeguards to ensure transparency, governance and proportional justification of its use.

Essentially, this document and its stakeholders should refer to it as a working document that continually monitors and governs the deployment and day-to-day use of such technology.

Facial Recognition Markets

The facial recognition market is a growing one, currently estimated at around £2.9 billion and predicted to increase to £5.6 billion by 2022. Factors such as rising applications in various industry sectors, growing smartphone implementation and increasing instances of identity threats are fueling the market growth. Facial recognition technology can resolve sector specific problems and is revolutionising industries such as policing, public security, transportation, gambling and retail.

Some of the newer systems are camera- agnostic and work with legacy CCTV for wide- range surveillance and identity verification.

This technology is also being integrated into access control applications, (either on a stand-alone door or integrated into a full system. This solution enables pre-enrolment of photos, enabling ticketless entry to stadiums and events, and speedier check-in at hotels, offices, gyms, airports etc.

Facial recognition technology is transforming airport security: government agencies (in conjunction with airlines) are aiming to improve efficiency when entering and exiting the country and deploy automated passport verification.  This trend is set to grow and is being trialed across other airport facilities and applications.

Retail is predicted to be a key growth sector as facial recognition is used to estimate a customer’s age, gender or mood so stores can target them with ads on in-store video screens or mobile applications. Banks are relying on facial recognition software to improve security, eliminate fraud and negate the need for ATM bank cards. The gambling sector is deploying the technology to scan and recognise those who come in and gamble. They can spot regulars who require VIP treatment, deny entry to pre-enrolled gambling addicts, cut down on people known for cheating and reduce incidents resulting in losses.

Stadiums, arenas, concert halls and other venues can be equipped with facial recognition cameras or kiosks enabling ticketless entry, crowd control and increased security, including identification of those who aren’t authorised to be there (ticket touts, disruptive supporters etc.).

Portable ruggedised deployments kits for rapid deployment and facial recognition glasses which enable a watchlist alert to a portable device/phone can be used for a variety of stand-alone policing and security events such as World Leader summits, public protests/demonstrations and annual sporting and event fixtures (G20, Wimbledon, Ascot, Glastonbury etc.).

This technology enables ticketless entry, crowd control and increased security surveillance.

The Future of Facial Recognition

NIST says that the advancement of deep neural networks has driven ‘an industrial revolution’ in facial recognition.  Many prestigious buildings and landmarks in the City are evaluating it and a number of iconic London buildings are running paid trials as part of building access control solutions. They have reported positive feedback from security personnel, clients and employees.

It brings many commercial benefits, enabling them to pre-enrol visitors, lessen workload for reception and security staff, identify VIPs (Whitelist) and potential security threats (Blacklist).

Facial recognition is the future and the next decade will see the widespread use of it across many industries.  If used in a measured, transparent and ethical way, facial recognition has the capability to significantly benefit all our lives.

Lorraine Antiri

LA Business Partnerships

Jane Mason

ISS Digital

www.issdigital.co.uk

The post Facial Recognition Security Technology: The Facts appeared first on City Security Magazine.

Facial recognition has been around for some time now, but like early biometrics (remember fingerprint scanning, hand/vein geometry and iris scanning) it’s yet to establish itself as mainstream technology.

There have been numerous reasons, both technical and psychological, that have had an impact on this necessary transition to facial recognition becoming a widely used accepted standard in the areas of border and counter terror operations.

The early technical reasons for non-adoption were obstacles such as the accuracy, enrolling faces and the processing power required to operate the solution.

For example, if you had a passport photo type image enrolled in a system, taken in good lighting and a clean background, and you subsequently had your image viewed by a recognition system with a camera taking your image in a similar, controlled environment, the chances were that an accurate result could be achieved to an acceptable extent.

However, this normally required your image to be processed by a powerful, centrally located computer system and all the associated networking, bandwidth and processing power. Remember the early automated recognition systems at UK airports? They certainly worked, but at their own arduous pace. Not only that, but there is an abundance of privacy issues of storing image databases centrally and serving the general public’s images and biometric information across these networks to the central processing points.

Border control areas

Unfortunately, when you put all this into a ‘real world’ scenario, it becomes clear that not all areas at border controls, or really any operation to provide counter terror benefits, could guarantee quarantined lighting and image quality, from a suitably high-resolution camera, and on moving multiple targets simultaneously. The older technology was simply not suited to everyday environments.

Consider the following: A car containing a driver, front passenger and two rear passengers, with a rain-spattered windscreen and tinted/darkened privacy glass, is driving at night past a border control point or a vehicle checkpoint.

Historically, this would require an optical specialist (to source suitable machine cameras capable of capturing multiple faces, some very special synchronised illumination to get through the darkened glass and ignore reflections and other unwanted detail on the glass of the vehicles, and a network connection to a central data processing point to carry out the facial detection and recognition processing against a known database.

All this, yet you still had to hope that the images in the database were of acceptable quality to be compared with the images from inside the vehicle (assuming they also were of sufficient quality, and the persons were actually looking at the camera).

Progress

Fortunately, there has been exceptional progress in the machine learning and digital recognition algorithms in the past years, for which the automated passport and facial recognition machines at international airports can certainly testify. Some of the world’s major corporations and leading names in both software and hardware have released, and are supporting, major facial detection and recognition solutions.

Furthermore, some real development and innovation has become available in the camera and processing areas that allows both substantially superior accuracy rates of detection and recognition, while also operating locally in the camera or local camera controller to provide ‘on the edge’ processing and operations.

‘Watch list’

In principle, this means that a camera and its integral processor can have a database of ‘watch list’ images (persons of interest for a valid and legal reason) loaded directly onto the camera.

The camera can then surveil vehicles or groups of people passing through its field of view.  The camera technology automatically detects faces, even through darkened and tinted glass, and ignores reflections. These faces are scored, with the best quality images of each face being further optimised and cropped out of the overall photo to be checked locally to the camera’s electronics against a known watch list.

The camera’s ability to watch for known faces locally on its own electronics package means that general members of the public, not on the watch list, have their faces compared locally by the camera and then instantly deleted. This not only loyally protects the privacy of the general public, but also drastically reduces network traffic and required processing, as only ‘watch listed’ faces that are of interest are now actually sent to a response team or location, optimising the efficiency and accuracy of the overall solution.

Facial recognition increasing security

These types of solutions are ideal for border control and counter terror operations (as they’re neither car, personal card or licence plate dependant) and are also proving of interest for fraud detection and reduction on ticket-based systems such as transport and rail networks.

Due to their proficient additional access control validation, solutions such as these are already in use at high security sites such as military bases and borders. Already, they’re proving successful at both increasing throughput (reducing the danger of choke points and queues) and increasing overall security and safety.

We’ve had our licence plates recorded for several years, and now even standard car parks use this technology. Simply put, it’s the next logical step in the field of security that allows the uniqueness of each of our faces to ensure our safety, while simultaneously respecting our privacy.

Dave Harmon

Business Development Director UK and Europe

Gatekeeper Security Inc.

www.gatekeepersecurity.com

The post Facial recognition for border control and counter terror appeared first on City Security Magazine.

The problem of throughput…

You are a busy facilities manager/security manager in a city or confined environment; therefore, you have very limited space for traffic lanes entering and leaving your location.

You cannot afford to have traffic backing up onto public roads from your facility, but you need to balance this with checking drivers’ details and the vehicle details (with possible vehicle inspections) whilst still ensuring security is maintained at your location.

Currently, it is most likely that you operate an access control system (personal card or vehicle based tag and automatic barriers) and you may have a separate additional check by a guard on the vehicle.

Whilst the card or vehicle tag system speeds up operation, it doesn’t stop a different driver using someone else’s car or card to gain access to your facility, thus fully compromising your perimeter/entrance security.

In the case of higher security locations, you may operate by using guards to check both the driver’s (and passengers’) ID and the vehicle number plate against the guard’s database and knowledge of who is allowed on site. And in the highest security areas or in time of heightened threat levels, you may require the vehicle and its underside to be searched (usually by mirrors on handles and by eye).

Studies have shown that this manual process of a guard checking credentials results in a relatively slow throughput: at best, throughput is approximately 6 vehicles per minute, per lane, with each vehicle requiring 10 seconds for basic ID check (this does not allow for under-vehicle check).

If there is a need to visually check the underside of the vehicle, that can add 20 to 60 seconds depending on vehicle size.

The only solution using guards is to increase the number of lanes entering and leaving, which is both expensive and often impossible in city areas where space is just not available.

The problem of security…

In addition to low throughput, the traditional method of using a guard for manual checking may not offer the best security for either your facility or for your security personnel.

Traditional ID checks are relatively difficult because of the steps involved, as for each vehicle the guard must carry out a range of checks and inspections, including ID card check for all occupants of the vehicle and checks of its contents.

For good security to be maintained, and in busy periods, this may entail the guard having to repeat the process several hundred times per day and never failing in the process or checks.

Adding electronic assistance such as card readers can verify the card to be authentic and even call up a picture of the cardholder on a local monitor, but it still needs the guard to visually verify the person and that all adds more time to the equation.

Recent terror attacks prove that whilst the inside of the facility may be secure, the traffic and personnel outside present a route in and a target in themselves.  Improving throughput is important for the security of both the site and the personnel.

Most importantly, as the guard’s attention is divided between facility safety and their own personal safety, the safety of everyone on the site is not optimised.

The answer to slow throughput…

In an ideal world, every security guard would have a photographic memory and would recognise both the driver and the vehicle, be able to distinguish between normal and abnormal behaviour and any new information, like access being revoked for a particular driver.

With such a good memory and ability to access live and changing information from memory, the guard would be also able to wave authorised personnel and vehicles through and easily know when to stop unauthorised people and vehicles.

If the guard could do all the above, instantly and accurately, and all the time, both throughput and security would be greatly enhanced simultaneously.

Obviously, guards like this are very hard to find … and it is impossible to rely on them not to have to take time off or be put under duress or compromised in some other way.

But augmented with the right technology, every guard can have optimised capability to carry out the job in a secure and speedy manner, without making mistakes or missing vital information, using a combination of facial recognition and capturing the registration plate.

What does facial recognition offer?

In a large population, recognising everyone by sight is a rare human gift, but this is exactly what facial recognition does. Recent breakthroughs in academic research have finally enabled facial recognition technology to achieve its potential, which is to have the same discriminatory power (accuracy) as a single fingerprint.

Today’s best algorithms can successfully recognise a person’s face as being distinct from hundreds of thousands of other faces. Its accuracy far surpasses the capabilities of most humans. In terms of face images, the algorithm is robust to variations in illumination, resolution, background, pose, expression and occlusion.

On a basic laptop computer, the algorithm successfully recognises a face in an image file within one second, even when using a database of several million other faces.

By selecting the most similar match for a current camera image and requiring a minimum threshold for similarity, it is easy for the algorithm to recognise a particular person or determine that the person is unknown (not in the database).

Furthermore, the database can include lists of authorised personnel as well as various ‘watch lists’ if required, such as known protesters, criminals, and suspected terrorists. In this way, security personnel can be alerted early and in a co-ordinated manner when a threat appears.

Earlier facial recognition systems relied on images being taken in quarantined environments to give suitable image quality (think of having your photograph taken for your passport) and therefore required the subsequent images taken from other camera sources in future to be of similar quality and view … this was never going to be viable in the real-world scenario.

It certainly wouldn’t have supported cameras on streets (in hugely varying lighting conditions and weather) or trying to look through darkened glass of cars or reflective windscreens on vehicles.

In the real world for vehicle gates/barriers and roadside operation, the system must be able to capture and recognise a face through a windscreen, even when the windows are tinted, day and night, rain or shine, and at some distance.

Getting a good image under those conditions requires an optimised camera designed for this process and with a specialised and optimised illuminator.

Fortunately, that technology has been successfully developed and is currently deployed in use at hundreds of sites worldwide. It can show driver and passenger faces through the heaviest of tinted windows and is not affected by many differing weather conditions up to distances of 15 metres.

Capturing the Registration Plate for added security combination (2-factor detection)

Two-factor authentication increases security greatly by improving recognition accuracy of authorised drivers and passengers. For the system to fail, both detectors (face recognition camera and registration plate camera) must fail. Hence, the probability of error for the combined recognition system, using two detection methods, is the product of the individual detector probabilities of error.

For example, if both facial recognition camera and registration plate camera have a 95% probability of accuracy, the dual-detector (dual camera system) has a probability accuracy rate of 99.75%

In addition, multiple detectors increase the robustness of the security system. If there is a technical problem reducing capabilities of one piece of equipment, the other parts of the system can still provide some security assurance.

To recognise cars, several technologies are effective … the most flexible being a registration plate reader, as it doesn’t require any co-operation by drivers, or any additional equipment for their cars. Registration plate reading technology is now widely used and very effective in lanes or when approaching gates and barriers.

Again, the system utilises a camera aided by LED lighting to get a good registration plate image and also Optical Character Recognition (OCR) tuned for accuracy in reading registration plate characters.

Registration plate readers also use watch lists of known vehicles, including those owned by authorised employees, and, if required, can use those known to be associated with protestors, criminals and suspected terrorists.

Generally, for operation at a specific location, two cameras are required, one for facial recognition and one for registration plate reading. The two cameras must be directed at different angles and positioned in different locations, so often two discreet mounting poles are used (if the cameras cannot be fitted to the building’s fabric at the entrance).

If rear-seated passengers are to be detected, an additional two cameras is normal, and if the underside of the vehicle is to be captured and available for instant comparison in future, a scanning set of cameras is fitted on the roadway.

To fully automate the system, a pressure sensor or beam device can be used to detect each passing vehicle and trigger the cameras at appropriate times, allowing the system to automatically:

• record images (of registration, driver and passengers) and under-vehicle image (if fitted)
• immediately initiate facial recognition and registration plate recognition (and under vehicle image recognition if fitted)

All of the above takes less than 3 seconds.

If the database of authorised personnel is available, the system will immediately check driver, passengers and vehicle images … this is instant.

If the images of vehicle, registration plate and driver/passengers are not in the database already, they can be automatically captured and stored (enrolling the driver with the vehicle’s details) whilst security is assured using the manual standard operational process by the guard. Once the guard is satisfied that the driver/passengers and vehicle should be granted access and be enrolled on the system as authorised, this can be done quickly and immediately, and in future the system will automatically check and grant/deny access on the basis of the updated information given to the database.

In this way, the system is self-learning and a database can be built up of authorised personnel and their associated vehicles.

In the relatively rare case that an unauthorised driver and a vehicle must be turned around at the gate/barrier, the guard will be able to annotate that unauthorised driver’s photo and vehicle registration by clicking an icon on the photo, and the database can automatically record/add that person to a ‘watch list’.

This method of creating a database through use is akin to machine learning. It makes the system quickly capable whether facilities have existing data or not (very useful for building databases of visiting delivery vehicles or maintenance contractors) whilst eliminating the problem that an existing database might contain old or otherwise bad data.

Over time, enrolment can be tapered off, updating information for authorised drivers only occasionally to maintain current images.

In addition to the added security described above, should the threat level or security requirement warrant it, the system can be augmented to operate with ‘automated foreign object detection’ under the vehicle and X-Ray of the vehicle itself to detect anomalies inside the vehicle.

Drivers are unlikely to object to this use of facial recognition technology because they get immediate benefits: Faster Throughput and Better Security.

Dave Harmon

Director, Business Development, UK & Europe

Gatekeeper Security

www.gatekeepersecurity.com

For more articles on facial recognition, see Security Technology

The post Facial recognition and registration plate reading appeared first on City Security Magazine.

Facial Recognition Technology doesn’t have to be 100% accurate to be useful – just hundreds of times better than the human brain.

Tremendous challenges face our police and security services when tracking suspected terrorists, keeping an eye on people living in our country and spotting threats coming across our borders – very often through legitimate routes like air-travel, but using fake documentation.

Humans are naturally very good at spotting people they know, even from small glimpses, but are poor at recognising those unfamiliar to them, so blanketing our transit hubs and public spaces with teams looking for suspects is a fruitless and unaffordable exercise.

Facial recognition technology

Through recent significant advances in facial recognition and other identification technologies, automatic alerting is finally becoming a very practical tool.

Most facial recognition solutions are “one-to-one”, where systems are used to confirm an identity in relation to a presented document – such as a passport or a work security badge.  So, the test is fairly simple – is the person standing in front of the camera the same one recorded in the passport?  For “one-to-one” facial recognition, there is no need to search against a database of millions of people that have valid passports – just a comparison between two photographs.

This challenge is made as simple as possible by utilising a high-quality passport photograph, good lighting, perfect camera height, and no difficult items covering the face, such as hats, for the comparison picture.  Yet, it often still fails. But, if you watch enough Hollywood movies, we are led to believe that governments can find wanted people instantly using low quality street cameras, dispersed across a city.

The leading-edge capability to find a “person of interest” in a crowd, or standing in an immigration line, is a significant leap from the technology used in passport e-gates – it presents ground-breaking capabilities, even if not quite at the level of Hollywood’s imaginations.

We refer to this new type of check as “one-to-many, non-compliant”, meaning that we are searching for the person amongst a larger database of suspects, and they are not presenting themselves in perfect conditions to the camera – in fact, they may not know there is a camera there at all.

Advanced enough

A camera watching a potential target area for a terrorist attack is unlikely to have perfect conditions – the resolution of the face will be lower even though we may be using a high definition camera because the subject will be at distance, and most probably moving. The face may be in partial profile and without a neutral expression. The subject may be wearing a hat, the lighting may be poor, or their face may be obscured by hair. I am often asked, “How accurate is the potential of facial recognition in these circumstances?”, with an expectation that it will be close to 100%.

However, I like to turn the question round and say how much better does technology need to be than a human brain? Is hundreds or thousands of times better, even if it does not boast 100% accuracy, a useful tool? We are close to the point that facial recognition technology on one camera can out-perform a hundred people watching that feed, and that gives us interesting options.

So what has driven the development of technology used to more effectively find people in a crowd?

Last year there was a lot of noise regarding machine learning being used to assist Google’s AlphaGo program to beat Lee Sedol, the top ranked go-player.  New facial recognition technology is using the same machine learning capabilities to improve its detection accuracy, and critically, its speed of detection, to improve performance. It is specifically focused on identifying people in a crowd, using video as a key differentiator, whilst many traditional systems treat video as a collection of still images, and extract the most “passport-like” image from the video to compare against. By using multiple images when tracking a subject, new technology increases its detection accuracy, because each frame gives out new information.

Advantages

Humans do still have a role – the level of involvement depends upon the criticality of selecting a target from video. If seeking out a white-collar criminal who has missed a court hearing, for example, I would set the system with a high threshold as too many sightings for a low tier case would result in police officers’ time being wasted. But, with strong intelligence that a suspect has travelled to a major city with a bomb, I’d want to bring all sightings that are fairly close to that person’s likeness to the attention of the police. The ability to dynamically set the “importance” of detection, the relationship between false alerts and false rejections, is critical.

Of course, there are understandable objections regarding privacy and appropriate levels of monitoring.  But, it is important to recognise that these systems are not deployed to track “unknowns” – they are simply being used to compare people against suspected persons in a set of existing watch-lists, and sightings of normal public can be discarded if no match.

Lists of people who should be excluded from our borders already exist within our agencies, and most people would likely be much more comfortable if everyone entering our borders received an automatic check performed against those lists.

Cutting-edge technology allows that check to be performed whilst people are waiting in line, prior to reaching an immigration officer, ensuring that wait times and processing burdens are not impacted.

Finally, it is not just governments that can take advantage of this technology. Security managers in our cities need to be vigilant against threats, and many are provided with lists of persons to watch out for.

This same technology can be applied within buildings on CCTV cameras and smartphones to look for excluded persons and raise immediate warnings to security teams.

We are even seeing applications of facial recognition for fast track, touchless entry at building security gates. It looks like facial recognition is finally coming of age.

Mark Patrick

Chief Technology Officer, Digital Barriers

The post Facial recognition: cutting-edge technology appeared first on City Security Magazine.

Facial recognition software is present in more places than you might think. TV shows with security themes or crime storylines are full of it and watching these types of television programmes shows you how much it can do to help solve crimes and track wanted individuals.

However, what you see on the television screen, as I’m sure you can imagine, is not quite as easy in real life.

Facial recognition to reduce crime

In the past, police cameras have been installed to attempt to cut down on crime by using facial recognition technology. However, those in Tampa Bay, in the USA, failed to make any difference to the crime rates in the city and were scrapped after two years because of their ineffectiveness. Due to the publicity surrounding the installation of these cameras, people were seen to be passing the mounted equipment wearing masks, covering any recognisable parts of their bodies, and making gestures to the cameras that suggested they did not appreciate them being installed in the first place. This meant that very little identification of criminal activity could be carried out.

A decade ago, there was also a similar system put on trial at Boston’s Logan International airport, where volunteers were asked to have their faces scanned at security checkpoints, but, unfortunately, the results from the trial were proved to be only 61.4 per cent accurate.

However, despite these previous disappointments, security companies are embracing the technology once again, as it has significantly improved since it first became popular in the 2000s. In fact, recently the technology has developed to the point where companies can use facial recognition software to recognise those whose faces have even been partially covered.

Improved facial recognition technology

Previously, the technology could only be used to recognise faces from the front, but, with the advances made in its development, it can now be used to recognise faces from various angles. The software is now far more accurate and this has gone a long way to alleviate concern about false positives. Already used by security companies around the world, immigration officials and other organisations in charge of safety and security, the technology has also entered the academic world as an archive search tool, as it can be used to look through old photographs and videos.

Although the development of facial recognition technology has been ongoing for many years, you might be surprised to learn about the different places that it is already being used, and what this could mean for the future of the technology, and the future of security itself.

New uses for facial recognition

For starters, facial recognition software has been, and can be used for personal reasons, such as online dating. There are companies out there on the internet that will take a picture of your face and match it with the face of somebody with whom they think you are well matched. This facial recognition tool aims to read the individual’s face and discover how compatible they will be with other clients. In a light hearted way, another online tool will try to find your doppelganger, but in dog form, rather than human form.

Another slightly less conventional use of facial recognition technology would be to use it for something that is not a face at all. An application that is available for both tablets and smartphones has developed a function that uses the software to read a picture of a leaf, and tell the photographer what species of tree it has come from.

This is useful for landscape architects and biologists, but perhaps not all that interesting for those of us who are still struggling to tag all of our friends on Facebook.

Like Facebook, advertisers have been quick to recognise the commercial uses for facial recognition software. It is only legitimate concerns about personal data which are preventing truly personalised advertising from appearing on screens in public places.  Advertising which appears as you pass and which is targeted at your demographic already exists on digital billboards. For example, digital signage company Amscreen has rolled out these kinds of adverts to Tesco petrol station forecourts, although it is based on facial scanning software to determine your demographic, not facial recognition software which would identify you personally.

Facial recognition in public sector

Use of the software in the public sector has also moved on. Police are currently beginning to roll out the use of a software called MORIS (Mobile Recognition and Information System), which is a device that slides over the top of a touch screen device, such as an iPhone. The device will read fingerprints from a glass screen as well as scan retinas in photographs to help identify people. This tool uses multiple forms of recognition software, including facial recognition, to help police investigate crimes and misdemeanours.

Whatever you think about the ways in which this software is used, there is no denying that the development of facial recognition in recent years has overcome many real world problems when it comes to safety and security.  Whilst many people might be wary of the Big Brother aspect of this kind of surveillance, and may be concerned that we are being watched all of the time, the technology and its application have been developed to provide safeguards, including those enshrined in law, when it comes to reading the faces of the public.

Security companies need to consider their duty to the general public when they think about using this form of software due to the privacy issues involved; the people on whom the cameras are trained need to be aware that it is happening. However, as we are now able to discover the perpetrator of a crime simply from reading an image, no matter how unclear, the pros may well outweigh the cons on this one.

Sonia Blizzard MD, Beaming

www.Beaming.biz

The post Reducing crime with facial recognition systems appeared first on City Security Magazine.