Expression Recognition Method Based on Attention Neural Network

Convolutional neural network has been widely used in facial expression recognition. However, the expression ability of facial expression features extracted from convolutional neural network is insufficient, the recognition accuracy is not high, and the network parameters are large. In order to solve these problems, an expression recognition method based on attention mechanism and residual network is proposed. In this method, the expression image is processed by dual pooling channel attention module to deepen the expression of discriminant features, and then the residual structure convolution layer is used to extract features. Finally, Softmax is used to realize expression classification. The experimental results show that compared with the original ResNet model, the recognition effect is better, and the recognition performance is improved by 4.14% and 6.07% on CK+ and JAFFE facial expression data sets, respectively. Compared with other attention models, it is lighter and has better recognition performance.