View and texture-independent facial expression recognition in videos using dynamic programming

In this paper, we address the recognition of facial expressions in continuous videos. We introduce a fast and texture-independent approach that exploits facial action parameters estimated by an appearance-based 3D tracker. We represent the learned facial actions associated with different facial expressions by time series. We adopt a dynamic time warping technique for recognizing the facial expressions using similarity measures between the learned time series and the tracked ones. The developed approach can be used online and can deal with non-frontal face images since it is view-independent. Preliminary experiments demonstrated the effectiveness of the developed method.