Facial expression recognition from image sequences using twofold random forest classifier

Abstract

We propose a novel framework for facial expressions analysis by recognizing AUs from image sequences using twofold random forest classifier in this paper. The measurement of facial motion is through tracking of Active Appearance Model (AAM) facial feature points using Lucas–Kanade (LK) optical flow tracker by estimating the displacements of the feature points. The displacement vectors between the neutral expression frame and the peak expression frame are used as motion features of facial expression. They will then be transformed to the first level random forest to determine the Action Units (AUs) of the corresponding expression sequences. Finally, the detected AUs are inputed into the second level random forest for facial expressions classification. The experiments on Extended Cohn–Kanade(CK+) database demonstrate that the proposed method can achieve higher performance than several other approaches on both AUs and facial expression recognition. We attain an average recognition rate of AUs and facial expression of 100% and 96.38% respectively.