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.