Convolution neural network for automatic facial expression recognition

Facial expression recognition is a hot research direction of pattern recognition and computer vision. It has been increasingly used in artificial intelligence, human-computer interaction and security monitoring in recent years. Convolution neural network (CNN) as a depth learning architecture can extract the essential features of the image, and in the case of large changes in shooting conditions, its effect is better than the traditional methods of Support Vector Machines(SVM) and Principal Component Analysis(PCA). Therefore, an improved method of facial expression recognition based on CNN is proposed in this paper. The purpose is to classify each facial image as one of the seven facial expressions considered in this study. According to the characteristics of facial expression recognition, a new convolution neural network structure is designed which uses convolution kernel to extract implicit features and max pooling to reduce the dimensions of the extracted implicit features. In comparison to AlexNet network, we can improve the recognition accuracy about 4% higher on the CK+ facial expression database by the aid of Batch Normalization (BN) layer to our network. A facial expression recognition system is constructed in this paper for the convenience of application, and the experimental results show that the system could reach the real-time needs.