Neural information processing in real-world face-recognition applications

One of the challenges of neural information processing is to achieve, at least partially, a similar performance to humans on systems for automated visual face recognition. Commercial applications of new technology don´t care about the underlying architecture or paradigms. The architecture simply needs to work all the time and be easy to use. That was the essence of ZN´s product design plan when we began developing our ZN-Face access control system. ZN-Face relies on von der Malsburg´s graph matching, which is robust enough to deal with the low quality pictures encountered outside the laboratory when developing automated image acquisition from real world scenes. (In this way, of course, the underlying neural system´s robustness is essential, because otherwise we could not have fulfilled the works-all-the-time requirement.) At ZN, we developed the complete hardware and software setup for the biometric access control device. We optimized and adapted the algorithms to the specific verification task-that is, “is the person in question identical to the cardholder?”and tested it in the hardware setup, leading to a 99.5% performance verification rate. As with most face recognizers, ZN-Face requires the cooperation of users, who must orient their heads toward the camera during picture acquisition (±15°). Today´s algorithms can only partially solve the challenge of generalizing from, for example, a half profile view to a frontal view