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.
Recognizing microexpression serves as a vital clue for affective estimation. Fast and discriminative feature extraction has always been a critical issue for spontaneous microexpression recognition applications. A microexpression analysis framework is proposed by adaptively key frame extraction and representation. First, to remove redundant information in the microexpression video sequences, the key frame is adaptively selected on the criteria of structural similarity index between different face images, Second, robust principal component analysis is applied to obtain the sparse information in the key frame, which not only retains the expression attributes of the microexpression sequence, but also eliminates useless interference. Furthermore, we construct dual-cross patterns to get the final microexpressions representation for classification. Repeated comparison experiments were performed on the SMIC and CASME2 databases to evaluate the performance of the proposed method. Experimental results demonstrate that our proposed method gets higher recognition rates and achieves promising performance, compared with the traditional microexpression recognition.