Face Inpainting Algorithm Combining Face Sketch and Gate Convolution
Fuping Wang, Yang Hu, Weihua Liu, Ying Liu · 2022 4th International Conference on Natural Language Processing (ICNLP) · 2022
Video surveillance contains a lot of facial occlusion, which brings great difficulties to the detection of criminal investigation cases. Current face inpainting algorithms are difficult to meet the uniqueness requirements of face comparison, due to the lack of a priori information within the occluded area. Face sketch drawn by experienced simulated portrait artist according to low-quality video or description of the victim contains lots of useful information. There, this paper proposes a face inpainting algorithm combining face sketch and gate convolution. First, the face sketch, used as guided information, integrates into the occluded face image to complete the missing area. Then, a generative adversarial networks (GAN) with gate convolution is designed for model training, which effectively suppresses the interference of the occlusion area to the inpainting process. The experimental results show that the proposed algorithm obtain the better inpainting results and larger SSIM compared with the other algorithm. The proposed obtain better comprehensive performance.