LOCAL OCCLUSION FACIAL EXPRESSION RECOGNITION BASED ON CHANNEL ATTENTION MECHANISM
The part of the face is blocked by various obstructions,resulting in a decrease in the recognition rate of facial expressions.Aiming at this problem,this paper proposes a local occlusion facial expression recognition method with discriminative residual network.The channel attention mechanism was inserted in each residual block of the residual network and after the last residual block to obtain a new residual network model that did not included a fully connected layer,and a channel-dependent feature map was obtained through the model.The island loss function with discriminable characteristics was introduced in the fully connected layer of the residual network,which was combined with the loss function of the softmax to classify the output features.Different algorithms were used to recognize the facial expression after occlusion processing.The results show that the highest recognition rate of the discriminative residual network on the Jaffe and CK+data sets after occlusion processing is 97.6％and 95.4％,respectively.This method can effectively improve the recognition of partially occluded facial expressions to a certain extent.
万方数据
维普
