Multiscale Convolutional Attention-based Residual Network Expression Recognition

Expression recognition has wide application in the fields of distance education and clinical medicine. In response to the problems of insufficient feature extraction ability of expression recognition models in current research, and the deeper the depth of the model, the more serious the loss of useful information, a residual network model with multi-scale convolutional attention is proposed. This model mainly takes the residual network as the main body, adds normalization layer and channel attention mechanism, so as to extract useful image information at multiple scales, and incorporates the Inception module and channel attention module into the residual network to enhance the feature extraction ability of the model and to prevent the loss of more useful information due to too deep network, and to improve the generalization performance of the model. From results of lots of experiments we can see that the recognition accuracy of the model in FER+ and CK+ datasets reaches 87.80% and 99.32% respectively, with better recognition performance and robustness.