Trending Now: The Faces of Facial Recognition

Blog Date:  1/28/2017
Author:  Ray Coulombe

With significant advancements in available processing power and artificial intelligence (AI), there is no sign that facial recognition will abate. Recently, General Motors announced a system that will monitor a driver’s face to determine if he/she is actually paying attention to the road while the vehicle is in cruise control—effectively using AI to monitor certain behaviors instead of for authentication purposes. In addition, Australia is promoting the use of facial recognition software in airport security by 2020 so it can maintain a more proactive approach to security.

However, not all facial recognition is created equal. Here’s why:

Basic facial recognition has been around forever. Initially, it was a manual process relying on human judgment to compare from memory or photos. Now, it’s a technology using distinguishable facial features for the purpose of identifying and, possibly, authenticating a person.

There are different types of facial biometrics for static (stationary) and dynamic (in motion) applications.

Static Applications
Static applications normally start out with a still photo of varying resolution, which can already create problems. The more efficient means of analysis is to examine the picture using various reference points to get to some degree of normality, extract the key parameters (such as distance between the eyes) and digitize those significant elements into files of limited size. Further problems include major opportunities to spoof the program by simply holding up a picture of the authenticated person in front of the camera.

One system effectively addresses those practical concerns. It called the Stone Lock Pro system by Stone Lock Global, which employs near-infrared (NIR) light to sample more than 2,000 points on a face – measuring the reflected energy from each point. Because NIR actually penetrates a short distance below the skin surface, the points have characteristically different reflective properties and generate different values based on reflectance. The result? A unique and non-spoofable file—even for identical twins. Its False Acceptance Rate (FAR) is <0.0004%, although there have been no known spoofs of the system to date.

Dynamic Applications
Taking on the in-motion challenge is FST Biometrics, based in Israel. Again, FST starts out with a fixed, high-resolution picture. Using various facial features, a set of “vectors” is derived and contained in a 2 KB file. The primary FST use case is grabbing multiple images from a video feed and creating vectors from each face to compare against the database of possible vectors (N:N). This is computationally intensive, and it is very difficult; yet, for this in-motion application, FAR of <.03% and False Rejection Rate (FRR) of <.2% are achieved. The result of a false rejection may just require another pass before the camera. The system is reliable because FST does not rely solely on the facial vector comparison to make their In Motion Identification system operate successfully.

The FST AI system instead learns “body behaviors” extracted from the visual data it is constantly accumulating. Physical attributes and personal tendencies are accumulated into their own vectors that, along with the facial vector, form a user record. These vectors work together in a flexible way to make a decision on a match.

Given advances in facial recognition and other unique biometrics, organizations should consider these as candidates for one of their authentication factors in a multi-factor system.


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