关键词: 生成对抗网络, 遮挡人脸, 人脸填补, 卷积神经网络

Abstract:

Local occlusion often occurs in face images in practical applications, which will result in lower recognition rate and robustness. Therefore, in view of the current situation, an expression recognition model based on improved Generative Adversarial Network（GAN） is proposed. Firstly, the occluded face image is filled and repaired by the antagonistic learning of the generator composed of automatic encoder and two discriminators（local discriminator and global discriminator）. Then, after the global discriminator, multi-classification layers are added to make use of the part of the global discriminator. The expression classifier is composed of convolution layer and multi-classification layer. Finally, through doing experiments, comparing the recognition rates of the expression and different algorithms between facial images with different occlusion areas before and after the occlusion area is filled. The experimental results show that the recognition rate will be higher, especially the recognition rate of large area occlusion.

Key words: Generative Adversarial Network（GAN）, occluding face, face filling, convolutional neural network