Integration of geometric and dynamic features for facial expression recognition in color image sequences

This work proposes a new approach for facial expression recognition in color image sequences, based on integrated evaluation of geometric and dynamic features. For this purpose a series of methods is introduced that on the one hand achieve high recognition rates for expressive facial behavior and on the other hand address a couple of common problems in this area of research. In particular we apply physiologically motivated image regions for the detection of dynamic features by using an optical flow method. In this way dynamic features capture the variations caused by facial expression changes. Opposed, geometric features do not contain temporal information but describe spatial feature parameters. These correspond to 3-D based Euclidean distances and angles. Particularly, the hypothesis of this work is that through integrated evaluation of geometric and dynamic features, improved recognition rates can be achieved. Based on comprehensive experimental investigations we show the advantage of the suggested approach.