Automatic recognition of facial expressions using Bayesian belief networks

The current paper addresses the aspects related to the development of an automatic probabilistic recognition system for facial expressions in video streams. The face analysis component integrates an eye tracking mechanism based on Kalman filter. The visual feature detection includes PCA oriented recognition for ranking the activity in certain facial areas. The description of the facial expressions is given according to sets of atomic action units (AU) from the facial action coding system (FACS). The base for the expression recognition engine is supported through a BBN model that also handles the time behavior of the visual features.