Facial micro-expression recognition based on local region of the key frame
Wenjun Zhong, Xinhe Yu, Ling Shi, Zhihua Xie · 2020
Traditional studies on micro-expression feature extraction primarily focused on global face from all frames. To improve the efficiency of feature extraction, this paper proposes a new framework based on the local region and the key frame to represent facial micro-expressions. Firstly, the face feature point detection technique is used to acquire the coordinates of the 68 key points, and the region of interest is divided by those key point coordinates and the action unit. Secondly, in order to remove redundant information in the micro-expression video sequence, structural similarity index (SSIM) is used to select key frames for each local region of interest. Finally, the dual-cross patterns (DCP) are extracted for the local regions of interest and are concatenated into a feature vector for the final classification. The experimental results show that compared with the traditional micro-expression method, the proposed method has higher recognition rate and achieves better time computation performance.