AU Data Augmentation Method Based on Generative Adversarial Networks

Abstract

In the facial action unit (Facial Action Unit, AU) recognition process, due to the low occurrence probability of some AUs, the sample imbalance is serious, which severely limits the model recognition performance. Generative adversarial network GAN is an unsupervised learning method. Compared with the autoencoder and autoregressive model in the unsupervised learning method, its advantages are sufficient data fitting, higher efficiency and better generated samples. The original GAN model uses the minimum and maximum (minmax) to continuously optimize the training of the model; the conditional generation adversarial network CGAN adds conditional constraints to the model input to make the generated results controllable and prevent collapse in the model training process. GAN has been widely used in research fields such as image processing, natural language processing NLP, and real-time color correction of underwater images. This paper designs a model based on a conditional generation adversarial network to supplement the minority samples of a specific AU and improve the sample distribution space of the action unit.