Four-player GroupGAN for weak expression recognition via latent expression magnification

Abstract

Facial expression recognition has a wide range of applications in the real world. Although many existing deep learning methods have achieved remarkable success, weak expression recognition remains a challenging task because of the significant domain gap between a weak expression and its peak expression counterpart. One idea to solve this problem is to find an effective way to bridge the gap between the two domains by either transfer learning or cross-domain image synthesis. In this paper, we propose a Group Generative Adversarial Network (GroupGAN) that recognizes weak facial expression by magnifying the expressions to stronger or peak ones. Different from the traditional GAN which typically has only one generator and one discriminator, the proposed GroupGAN has one generator, one extractor and two discriminators. Similar to the “two-player game” analogy of the traditional GAN, in our setting the generator along with feature extractor act as one group to compete with the other group of the two distinct discriminators. Extensive experiments show that the proposed GroupGAN significantly improves the performance of weak expression recognition, and is able to magnify weak expressions, thus facilitating many expression-related vision tasks like sketch recognition.