| Abstract | This presentation summarizes the effort of our group in designing a non Von-Neumann, biologically motivated approach to video processing and recognition. The need for such an approach is seen from the very fact that, while for humans recognition in video is easy, most recognition approaches developed to date still perform very poorly when applied to video data. Instead of focusing the effort on making the video data of better quality, as suggested by the Face Recognition Vendor Test Grand Challenge, we start from the premise that video data is inherently of bad quality, and hence the effort should be directed towards developing approaches which can deal with such low-quality data. We build our approach using the neuro-associative mechanism which is known to be of prime importance for biological vision recognition systems in enabling the accumulation of learning data in time and which is implemented by means of tuning the synaptic connections of a multiconnected neural network. As a result, we build a memorization-recognition system which can memorize a face from a video sequence and then identify this face in another video sequence. The testing of the system is deliberately done on video of very low quality, i.e. such that is just sufficient for humans to identify the faces. The application of the system for identifying computer users using a low-resolution webcam is shown. Other applications include video-annotation of TV programs and tele-conferences, and fusion of hard and soft biometrics for homeland security. |
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