Face expression recognition based on improved convolutional neural network

Abstract

Aiming at the problems of huge parameters and network degradation caused by simple linear stacked convolution layers or continuous full connection layers in traditional expression recognition methods, two convolution neural network models are designed through depth separation convolution and residual module respectively to widen and deepen the network. Firstly, model A adopts depth separation convolution instead of regular convolution layer, and the global average pooling layer replaces the final full connection layer, utilizes the methods of dropout, batch normalization, activation function of PReLU and image augmentation to avoid over-fitting effectively. Model B adopts pre-trained ResNet50 model to extract facial features, magnifies the images twice by the SRGAN method. Using ensemble method to fuse model A and B, the accuracy is further improved. To verify the feasibility of the method, the model was tested on the FER2013 facial expression dataset, and the performance was compared with the other facial expression recognition algorithms. The final results showed the improved convolutional neural network (CNN) reached the advanced precision of 73.244% in FER2013 dataset, and the experiment data and the number of model parameters all proved the effectiveness of this method.