Multi-Scale Integrated Attention Mechanism for Facial Expression Recognition Network

A multi-scale integrated attention network（MIANet）is proposed to address the problems of difficulty in extracting effective features and complex network model parameters in the current ordinary convolutional neural network for facial expression recognition. Firstly, in order to increase the width and depth of the network while avoiding redundant calculations, an Inception structure is introduced into the network, which can be used to extract multi-scale feature information of images. Then, the efficient channel attention（ECA）mechanism emphasizes the regions associated with facial expression and suppresses the irrelevant background regions to improve the representation ability of important facial features. Finally, deep separable convolution is used in the convolution layer to reduce network parameters and prevent over fitting. Experiments on public data sets FER-2013 and CK+ with the proposed method show 95.76% and 72.28% accuracy, respectively. The experimental results show that the method has a good recognition effect and strong generalization ability, and it has a certain reference value in facial expression recognition in terms of network structure setting and parameter configuration. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.