Facial expressions provide an important behavioral measure for the study of emotion, cognitive processes, and social interaction, and contain diagnostic information for neurological disorders and depression. Facial expression measurement from video is less intrusive than EEG, EMO, ANS or brain imaging measurements as an indicator of emotional activity. The measurement is presently performed by human experts. The goal of this research is to develop an automatic system for recognition, measurement, and coding of facial expressions from video using computer vision technology. This project will utilize probabilistic models of dynamical systems to mod& the facial behavior underlying the observed image sequences. These techniques include hidden Markov models, and a new stochastic modeling technique developed by Movellan and colleagues called diffusion networks [38]. Diffusion networks offer the advantage over traditional dynamical models of allowing continuous time dynamics and continuous states. An automated system would make facial expression measurement more widely accessible as a research tool in behavioral science and investigations of the neural substrates of emotion.
