GAN Based Three-Stage-Training Algorithm for Multi-view Facial Expression Recognition

摘要

With the rapid development of deep learning, the performance on facial expression recognition (FER) has been greatly improved. However, multi-view FER, where the facial information is difficult to be fully acquired due to pose deflections, remains challenging. This paper presents a generative adversarial network (GAN) based three-stage-training algorithm for multi-view FER. Specifically, the frontal-view faces are firstly adopted to train a convolutional neural network (CNN) in an efficient way. Then, the pre-trained CNN is embedded in a well-designed GAN such that the faces with different deflections can be synthesized as the frontal-view ones containing expression features. The original and synthesized faces are finally fused and employed to retrain the CNN classifier such that the multi-view FER problem is efficiently resolved. Experimental results demonstrate that the three-stage-training algorithm can achieve better performance than some existing methods.