基于生成对抗网络的遮挡表情识别.

Aiming at the fact that partial occlusion affected facial expression recognition in practical applications, this paper proposed an expression recognition method based on generative adversarial networks (GAN) _ Firstly, this method filled and repaired the occlusion face images, and then performed the expression recognition. The generator of GAN was composed of a convolutional auto-encode r, the face images generated by adversarial learning between generator and discriminator were more vivid. The discriminator was composed of the convolutional neural network, which had good feature extraction ability. It added a multi-classification layer to construct the expression classifier, which avoided feature re-calculation. In order to solve the problem of insufficient training samples, this paper used the CelebA face dataset to train face filling and repairing, and pretrained the feature extraction part of the expression classifier. Experiments on the CK + dataset show that the face images after filling are real and coherent, and achieves a higher expression recognition rate. Especially it improves the recognition rate of large-area occlusion of the face. [ABSTRACT FROM AUTHOR]