Facial expression recognition based on deep convolutional neural network

Facial expression is a part of body language, which can often convey physiological and psychological reactions. Facial expression has always been a hot topic in the area of image analysis and pattern recognition, whose basic task is to model facial images to recognize human emotions at a certain time. Due to CNN’s strong ability on feature expression, the researches of facial expression recognition based on deep learning have developed rapidly. In this paper, in order to improve the accuracy of facial expression recognition which is low because of the complex environments and structure of the existing expression recognition network, we propose a facial expression recognition method based on region of interest extraction, which uses ROI pooling to extract the local features of different face regions to construct a fixed size shared feature layer. In addition, we also add L2 regularization and learning rate decay mechanisms to find the optimal solution. Quantitative and qualitative experimental outcomes show that it is an effective way to achieve the facial expression task.