LAUSR

Because of the different features of the same facial expressions from different angles, most of the methods are only suitable for face images, and the accuracy of facial expression recognition is low. Therefore, a multi‐angle facial expression recognition method based on generative adversarial networks (GAN) is proposed. Firstly, the depth regression network is used to detect the key points of the face image to achieve face alignment, so as to reduce the difficulty of feature extraction. Then, the image is input to GAN. The generator is composed of encoder and decoder, and a skip connection is designed. In the encoding phase, the generator can unlock the correlation between the facial expression image and the angle, and in the decoding stage, it can generate different angle facial expression images by adding other angle information. Finally, the multi‐angle facial expression images are sent to the convolution neural network for classification and learning, in which the loss weight is adjusted dynamically to improve the recognition accuracy by introducing resistance loss, recognition loss, content loss, and center loss. The experimental results on Multi‐pose illumination expression (PIE) and celebrities in frontal profile (CFP) datasets show that the performance of the proposed method is the best when the learning rate is 0.0002, and the recognition accuracy under different angles is higher than other comparison methods, so it has practical application significance.