Occlusion-aware face inpainting via generative adversarial networks

Face inpainting aims to restore the corrupted regions of face images due to extreme lighting variations, occlusion, or even disguise. This task becomes especially challenging, when the face images are taken in an unconstrained environment (i.e., with pose, illumination, and expression variations) and the type of corruption is not known in advance. In this paper, we propose a deep-learning based approach of occlusion-aware generative adversarial networks (GAN) for solving this problem. By utilizing GAN pre-trained on occlusion-free images, we are able to detect corrupted image regions automatically with the associated image pixels properly recovered. We produce promising performances on images from the benchmark dataset of LFW, and show that recognition of such face images would be benefited from our proposed approach.