A novel feature separation model exchange-GAN for facial expression recognition

Currently, with the rapid development of deep learning, many breakthroughs have been made in the field of facial expression recognition (FER). However, according to our prior knowledge, facial images contain not only expression-related features but also some identity-related features, and the identity-related features vary from person to person which often have a negative influence on the FER process. It is one of the most important challenges in the field of FER. In this paper, a novel feature separation model exchange-GAN is proposed for the FER task, which can realize the separation of expression-related features and expression-independent features with high purity. And the FER method based on the exchange-GAN can overcome the interference of identity-related features to a large extent. First, the feature separation is achieved by the exchange-GAN through partial feature exchange and various constraints. Then we ignore the expression-independent features, and conduct FER only according to the expression-related features to alleviate the adverse effect of identity-related features. Finally, some experiments are conducted on three famous databases with the FER methods proposed in this paper. The experimental results show that the proposed FER method can alleviate the interference of identity-related information through feature separation by the exchange-GAN and achieve excellent performance for the objects that have not appeared in the training set. What’s more, our method can obtain very competitive FER accuracy on the three experimental databases.