Facial expression recognition based on attention mechanism ResNet lightweight network

Aiming at the problems of large network model and low accuracy of ResNet18 network model in facial expression recognition,a Lightweight ResNet based on multi-scale CBAM(Convolutional Block Attention Module) attention mechanism (MCLResNet) is proposed,which can realize facial expression recognition with less parameters and higher accuracy. Firstly,ResNet18 is used as the backbone network to extract features,and group convolution is introduced to reduce the parameters quantity of ResNet18. The inverted residual structure is used to increase the network depth and optimized the effect of image feature extraction. Secondly,the shared fully connected layer in the channel attention module of CBAM is replaced with a 1x3 convolution module,which effectively reduces the loss of channel information. The multi-scale convolution module is added to the CBAM spatial attention module to obtain spatial feature information at different scales. Finally,multi-scale CBAM module(MSCBAM)is added to the lightweight ResNet model,which effectively increases the feature expression ability of the network model. In addition, a fully connected layer is added to the output layer of the network model introduced into MSCBAM,so as to increase the nonlinear representation of the model at the output. The experimental results of the model on FER2013dataset and CK+ dataset show that the parameters quantity of the model proposed in this paper is reduced by 82. 58% compared with ResNet18,and the recognition accuracy is better.