Abstract
Face Photo-Sketch synthesis is designed to generate an image contains rich personal information and details facial. It is widely used in law enforcement and life areas. Although some existing methods have achieved remarkable results, however, due to the gap between the synthetic and real image distribution, the synthetic image does not achieve the expected effect. To solve this problem, In this paper, we proposed conditional generation adversarial networks (CGAN) based on arts drawing steps constrain. We divide the process into two stages. In the first stage, we take the original face photos which concat noise z as input, generating stage 1 low resolution synthesize sketches. In the second stage take the stage 1 results and original face photo as inputs, yielding stage 2 high resolution synthesize sketches, which can express more natural textures and details. To add realism, we train our network using an adversarial loss. Experiments have shown that, Compared with previous methods, our results generate visually comfortable face sketches. And express more natural textures and details.
Supported by Project of Fujian Science and Technology Department 2019I0036.
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Xie, X., Xu, H., Weng, L. (2019). Face Photo-Sketch Synthesis Based on Conditional Adversarial Networks. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_7
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