Expression recognition from 3D dynamic faces using robust spatio-temporal shape features

This paper proposes a new method for comparing 3D facial shapes using facial level curves. The pair- and segment-wise distances between the level curves comprise the spatio-temporal features for expression recognition from 3D dynamic faces. The paper further introduces universal background modeling and maximum a posteriori adaptation for hidden Markov models, leading to a decision boundary focus classification algorithm. Both techniques, when combined, yield a high overall recognition accuracy of 92.22% on the BU-4DFE database in our preliminary experiments. Noticeably, our feature extraction method is very efficient, requiring simple preprocessing, and robust to variations of the input data quality.