Facial expression recognition based on improved residual network

Facial expressions are an important part of human emotional signals and their recognition has become an important topic of research in the field of pattern recognition. Deep learning based methods have achieved great success in the recognition of facial expressions. However, with the evolution of convolution neural networks and the increased network depth, these methods suffer from problems such as degraded network performance and loss of feature information. To address these problems, a novel facial expression recognition algorithm based on an improved residual neural network is proposed. First, a residual neural network is designed to extract deep features while retaining the shallow ones. This can effectively prevent the degradation of network performance. Moreover, when the gradient of the Rectified Linear Units activation function used in the residual module is 0, it will inactivate the neurons and cause a loss of feature information. To address this, the Mish activation function is used instead. The slight allowance for negative values in Mish improves the gradient flow. Next, an inception module is introduced to obtain richer feature information under the same receptive field. Finally, by conducting verification experiments on the public datasets CK+ and KDEF, the authors manage to solve the problems of degraded network performance and insufficient information from extracted features, achieving recognition accuracy rates of 96.37% and 93.38% on the two datasets, respectively.