Facial Expression Recognition with Contrastive Disentangled Generative Adversarial Network

In the field of facial expression recognition, facial expression features are highly similar to identity features, making facial expression recognition a major challenge for researchers, due to the high overlap of expressions and identity features in terms of space, structure, and feature expression. Moreover, the existing models lack a decoupling mechanism during extraction, making it difficult to understand the intrinsic meaning of expressions, which in turn affects the accuracy of expression recognition. To separate expression features from identity features, this paper proposes a method called the Contrastive Disentangled Generative Adversarial Network (CD-GAN). In this study, facial representation is defined as a combination of identity and expression, and different encoders are used to extract these features, respectively. Unlike the methods of direct feature extraction, this paper uses semi-supervised contrastive learning and adversarial training to distangle the entanglement of identity and expression features, thereby obtaining a disentangled expression representation. The model in this paper, through the method of de-entanglement, enables the model to learn the differences between expression and identity features, achieving the separation of expression and identity features. The experimental results show that the quantitative and qualitative results of the CD-GAN on field and laboratory datasets are comparable to those of the state-of-the-art methods.