Facial recognition and . . . increase security and throughput


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


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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.


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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.


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