Facial Expression Recognition Based on Spatial and Channel Attention Mechanisms

For the present, many technical problems exist in the convolution neural network for facial expression recognition, such as the complexity of convolutional face feature extraction, the difficulty in accurately recognizing the subtle feature changes of facial expressions, and the low automatic recognition rate of facial expressions. In this paper, a hybrid attention mechanism based on space and channel-Height Performance Module Implement(HPMI) attention mechanism is proposed to realize automatic facial expression recognition. The addition of this attention mechanism can enhance the weight of key features and make the model focused on the features which are useful for expression classification in the training process. An HPMI module based on spatial and channel-based mixed attention mechanism is embedded in VGG-16 network. This can effectively alleviate the overfitting phenomenon of the network, strengthen the useful information, suppress the useless information, promote the information flow between the key information of the image and the network model. At the same time, it can solve the problem of the inconsistency between the input dimension and the output dimension. The accuracy of the method in this paper is 98.97% and 88.44% on CK + and RAF-DB expression datasets. Experimental comparison shows that by embedding the HPMI module, the model can further enhance the learning of spatial and channel feature weight. Our experiments include 2 datasets for expression recognition and show an average improvement of 3.94% in the accuracy.