Aiming at the problems of complex parameters and low computational performance of facial expression network model

an expression recognition method based on Convolutional Block Attention Module-Depthwise Separable Convolution network is proposed. The network usage depth separable convolution is combined with the traditional convolution. The improved Inception module extracts different feature information through different branches while reducing the network parameters and improving the network operation efficiency. Finally

the convolutional block attention module is added

which can make the network focus on the key information when extracting features

so that the extracted feature expression information is more accurate and more conducive to classification. The simulation experiments on RAF-DB dataset and CK + dataset show that the network model Convolutional Block Attention Module-Depthwise Separable Convolution has a high recognition rate

and the number of parameters in the ablation experiment is reduced by 6. 4% compared with the traditional Convolutional Neural Network

which improves the computing performance.