Spectral Norm Normalized Network for Facial Expression Generation
Jia Zhang, Xiao Lan Yao, Yunlu Zhang, Ning Xu, Xiaofeng Liu · 2020
Facial expression recognition (FER) is widely studied in the area of man-machine interaction. But in the current research on expression recognition technique, the degradation of recognition performance is caused by the problem of small sample and unbalance of sample size in expression database. In this paper, we study the generation of facial expression image in generative adversarial model, focusing on the solution to meet the Lipschitz constraint. We propose to generate the facial expression images based on spectral norm normalized networks (SNN-EG). The method satisfying K-Lipschitz regular constraints, can expand the number of expression samples, enrich sample diversity, and achieve the solution for the problems of small samples and unbalanced samples. The KDEF expression dataset is validated and evaluated. Experiments show that the proposed method has a better effect on synthesis and recognition of facial expressions.