RankBoost with L1 Regularization for Facial Expression Recognition and Intensity Estimation

Abstract

Most previous facial expression analysis works only focused on expression recognition. In this paper, we propose a novel framework of facial expression analysis based on the ranking model. Different from previous works, it not only can do facial expression recognition, but also can estimate the intensity of facial expression, which is very important to further understand human emotion. Although it is hard to label expression intensity quantitatively, the ordinal relationship in temporal domain is actually a good relative measurement. Based on this observation, we convert the problem of intensity estimation to a ranking problem, which is modeled by the RankBoost. The output ranking score can be directly used for intensity estimation, and we also extend the ranking function for expression recognition. To further improve the performance, we propose to introduce l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> based regularization into the Rankboost. Experiments on the Cohn-Kanade database show that the proposed method has a promising performance compared to the state-of-the-art.