A dynamic approach to the recognition of 3D facial expressions and their temporal models

In this paper we propose a method that exploits 3D motion-based features between frames of 3D facial geometry sequences for dynamic facial expression recognition. An expressive sequence is modeled to contain an onset followed by an apex and an offset. Feature selection methods are applied in order to extract features for each of the onset and offset segments of the expression. These features are then used to train a Hidden Markov Model in order to model the full temporal dynamics of the expression. The proposed fully automatic system was tested in a subset of the BU-4DFE database for the recognition of happiness, anger and surprise. Comparisons with a similar system based on the motion extracted from facial intensity images was also performed. The attained results suggest that the use of the 3D information does indeed improve the recognition accuracy when compared to the 2D data.