Abstract: Aiming at the problems of the existing facial expression recognition algorithms, such as long time, slow convergence speed and low classification accuracy, the framework and internal structure of LeNet-5 network are optimized and improved, and a facial expression recognition method based on improved LeNet-5 is proposed. In order to extract more diverse features and improve the ability of feature expression, firstly, the number of convolution layer and pooling layer is increased to adjust the internal parameters of the network; secondly, the generalization ability of the network model is improved by batch normalization of convolution layer and full connection layer; finally, the three pooling layers are overlapped and pooled by the combination of maxpool_avgpool_avgpool. Experiments on FER2013 face expression database show that the improved model has higher recognition accuracy than the current algorithm.

Key words:  convolutional neural network, facial expression recognition, batch normalization, fully connected