Probabilistic framework for embedded face and facial expression recognition

We present a Bayesian recognition framework in which a model of the whole face is enhanced by models of facial feature position and appearances. Face recognition and facial expression recognition are carried out using maximum likelihood decisions. The algorithm finds the model and facial expression that maximizes the likelihood of a test image. In this framework, facial appearance matching is improved by facial expression matching. Also, changes in facial features due to expressions are used together with facial deformation patterns to jointly perform expression recognition. In our current implementation, the face is divided into 9 facial features grouped in 4 regions which are detected and tracked automatically in video segments. The feature images are modeled using Gaussian distributions on a principal component sub-space. The training procedure is supervised: we use video segments of people in which the facial expressions have been segmented and labeled by hand. We report results on face and facial expression recognition using a video database of 18 people and 6 expressions.