Micro-expression recognition by feature points tracking
Shuoqing Yao, Ning He, Huiquan Zhang, Osamu Yoshie · 2014
One of the most interesting aspects of facial expression analysis is recognizing micro-expression. In this paper, a new feature tracking and alignment approach for micro-expression based on FACS systems and Tracking Learning Detection(TLD) is presented. The basic point for detecting first frame feature point is based on Hough Forest, and in order to increase the accuracy, we extracted features by Local Binary Pattern(LBP) as initialization. Unlike many previous works, the proposed approach applies conceptual area in perspective of human cognition. And this approach aims to track the extracted features and quantifies changing trend of these points for analyzing micro-expression. To estimate our approach's rationality, we conducted experiments on the CASME and SMIC facial expression database. The results show that the proposed approach is effective and performs well in recognizing some specific micro-expressions. Furthermore, the proposed approach is more accurate than previous methods based on Temporal Interpolation Model(TIM).