Boosting encoded dynamic features for facial expression recognition

It is well known that how to extract dynamic features is a key issue for video-based face analysis. In this paper, we present a novel approach of facial expression recognition based on the encoded dynamic features. In order to capture the dynamic characteristics of facial events, we design the dynamic haar-like features to represent the temporal variations of facial appearance. Inspired by the binary pattern coding, we further encode the dynamic features into the binary pattern features, which are useful to construct weak classifiers for boosting learning. Finally, the Adaboost is performed to learn a set of discriminating encoded dynamic features for facial expression recognition. We conduct the experiments on the CMU expression database, and the experiment result shows the power of the proposed method. We also extend this method to the active units (AU) recognition, and get a promising performance.