Recognizing people's faces: from human to machine vision

As confirmed by recent neurophysiological studies, the use of dynamic information is extremely important for humans in visual perception of biological forms and motion. Apart from the mere computation of the visual motion of the viewed objects, the motion itself conveys far more information, which helps understanding the scene. This paper provides an overview and some new insights on the use of dynamic visual information for face recognition. In this context, not only physical features emerge in the face representation, but also behavioral features should be accounted. While physical features are obtained from the subject’s face appearance, behavioral features are obtained from the individual motion and articulation of the face. In order to capture both the face appearance and the face dynamics, a dynamical face model based on a combination of Hidden Markov Models is presented. The number of states (or facial expressions) are automatically determined from the data by unsupervised clustering of expressions of faces in the video. The underlying architecture closely recalls the neural patterns activated in the perception of moving faces. Preliminary results on real video image data show the feasibility of the proposed approach.