F


acial 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


Seeing is believing.


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.


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.


Leading the way in intelligent optical inspection and recognition solutions to secure borders, identify threats, and provide real-time actionable intelligence.


Gatekeeper Intelligent Security Solutions gatekeepersecurity.com


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