Facial Expression Recognition using Encoded Dynamic Features

In this paper, we propose a new approach of facial expression recognition. In order to capture the temporal characteristic of facial expressions, we design dynamic haar-like features to represent the facial images, and code them into binary patterns for the further analysis. Based on the encoded features, Adaboost is employed to learn the combination of optimal discriminant features to construct the classifier. The experiments carried on the CMU database show the promising performance of the proposed method.