关键词: 素描人脸合成, 生成对抗网络, 残差网络, 多判别器网络, 深度学习

Abstract:

Sketch face synthesis has important application value in the field of entertainment and criminal investigation. In order to solve the problem of fuzzy face details and lack of realistic sense in the traditional sketch face synthesis method, the CycleGAN framework is improved and a sketch face synthesis method based on multiple discriminator cycle generation antagonistic network is proposed. The residual network is used in proposed method as generating network model, and multiple discriminator are added in the hidden layer of generator to improve the performance of network to extract the detailed features of the generated image. The reconstructed error constrained mapping relation is established to minimize the distance between the generated image and the target image. Through the comparison experiment of CUHK and AR face database, it is proved that the performance of this method is obviously improved compared with that of the original CycleGAN framework. Compared with the current leading methods, the detailed features of the sketch images generated by the method proposed in this paper are clearer and more realistic.

Key words: face sketch synthesis, generative adversarial network, residual network, multi-discriminator networks, deep learning