Dynamic Facial Expression Recognition Based on ResNet and LSTM

Abstract

The face image data collected by ordinary face recognition system is usually static. In real life, to fully capture the state information of human emotion change, we need to collect dynamic face expression change data, which needs to capture a video sequence to reflect the process of emotion change. In this paper, we combine ResNet residual neural network and long-term memory network (LSTM) to extract features from video sequences and then recognize them. In the residual neural network, the residual element has a very good effect on the training and optimization process of deep CNN model, and shortens the convergence time of the model. Combined with the LSTM network, the captured dynamic sequence data is incorporated into the CNN model. Compared with the static single frame expression recognition system, the research of dynamic expression recognition has more application value.