Using Dynamic Bayesian Networks for Posed versus Spontaneous Facial Expression Recognition

Abstract

Automatic analysis of facial expressions is a complex area of pattern recognition and computer vision with many un- resolved problems, one of which is the distinction between posed and spontaneous expressions of emotions. Previous psychology research indicates that the temporal dynamics in the face are essential for distinguishing between posed and spontaneous smiles. There are six temporal characteristics which are important: morphology, apex overlap, symmetry, total duration, speed of onset and speed of offset. In this work, we propose to distinguish between posed and spon- taneous expressions by using Dynamic Bayesian networks (DBN) to model the temporal dynamics. The DBN provides a suitable framework to represent probabilistic relationships between and within the various types of temporal dynamics. Based on the temporal phases of four different Action Units (onset, apex offset and neutral of facial actions) and the six temporal characteristics from the psychology research, we build several DBN models to distinguish between posed and spontaneous expressions. We present experimental results from 50 videos displaying posed and spontaneous smiles. When the DBNs trained on the temporal characteristics are combined to provide a joint classification, we attain an AUC of 0.97.