Face expression recognition based on attention mechanism of convolution network
A convolutional network based facial expression recognition method was proposed to solve the problems of large reference number and weak recognition ability in facial expression recognition.The improved residual module was introduced to reduce the parameters and enhanced the attention to the expression area;The channel-space attention mechanism was used to assign the weights of different dimensions and positions to the expression regions extracted from the network,and the subtle feature information of the key points of expression was focused on;The refinement module was used to further extract the depth feature information.In order to obtain higher accuracy,the joint loss function was introduced to increase the out-of-class distance and reduced the in-class distance to improve the accuracy of expression recognition.The experimental results showed that the average recognition rate was 63.91％and 97.98％respectively,and the parameter was 11.34 M.Compared with VGG network and residual network,the model not only improves the recognition rate but also reduces the redundant parameters.
万方数据
维普
