| Abstract | There is a physiological reason, backed up by the theory of visual attention in living organisms, why animals look into each others' eyes. This is to illustrate the main two properties in which recognizing of faces in video differs from its static counterpart - recognizing of faces in images. First, the lack of resolution in video is abundantly compensated by the information coming form the time dimension. Video data is inherently of a dynamic nature. Second, video processing is a phenomena occurring all the time around us - in biological systems, and many results unravelling the intricacies of biological vision already obtained. At the same time, as we examine the way the video-based face recognition is approached by computer scientists, we notice that up till now video information is often used partially and therefore not very efficiently. This work aims at bridging this gap. We develop a multi-channel framework for video-based face processing, which incorporated the dynamic component of video. The utility of the framework is shown on the example of detecting and recognizing faces from blinking. While doing that we derive a canonical representation of a face best suited for the task. |
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