Real-time Expression Recognition from Dynamic Sequences of 3D Facial Scans

In this paper, we address the problem of person-independent facial expression recognition in dynamic sequences of 3D face scans. To this end, an original approach is proposed that relies on automatically extracting a set of 3D facial points, and modeling their mutual distances along time. Training an Hidden Markov Model for every prototypical facial expression to be recognized, and combining them to form a multi-class classifier, an average recognition rate of 76.3% on the angry, happy and surprise expressions of the BU-4DFE database has been obtained. Comparison with competitor approaches on the same database shows that our solution is able to obtain effective results with the clear advantage of an implementation that fits to real-time constraints.