Multi-scale depthwise separable convolution facial expression recognition embedded in attention mechanism

For facial expression recognition, traditional machine learning method features extraction is relatively complex, shallow convolutional neural network recognition rate is not high, and deep convolutional network is easy to cause gradient explosion or dispersion problems. This paper constructs the multi-scale deep separable expression recognition network with residual network which embedded in attention mechanism. Through superposition of multi-layer and multi-scale depth separable residual elements, facial expression feature extraction of different scales is achieved; in the meanwhile, CBAM attention mechanism was used to screen the expression features for the purpose of improving the expression of the weight of the expression features and weakening the noise impact of training data. The algorithm network model in this paper achieves accuracy of 73.89% and 97.47% in Fer-2103 and CK + expression data sets respectively, which indicates that this network has strong generalization. © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.