Deep-block network for AU recognition and expression migration

Human facial behavior is an important information for communication. The study of facial behavior is one of the significant research topics in field of psychology, computer vision and artificial intelligence. In order to improve performance of facial expression and action unit recognition, a face recognition method based on deep-block network proposed in this paper. First, to improve the network performance, we preprocess the input of the network facial image, which includes two operations: face detection and face standardization. Second, deep-block network regards facial parts as the core of expression recognition rather than the whole face and key areas are in charge of specific action units to abate the weak correlation bias, which results in better classification and regression effect. Last, with the purpose of reducing impact of image independent factors, relevant feature map is applied to recognize the associated facial action units, which can promote the accuracy of detection to a certain extent. Experimental results on CK+ and MMI show that proposed method can not only capture the correlation of whole face regions globally, but also can increase network speed caused by too few pooling layers.