One paper from MIRACLE research group was accepted by IEEE TIFS. The full name of IEEE TIFS is IEEE Transactions on Information Forensics and Security. It is a CCF-A journal with the most recent IF of 6.013.
Occlusions are often present in face images in the wild. Existing face de-occlusion methods are limited as they require the knowledge of an occlusion mask. To overcome this limitation, we propose an OA-GAN for natural face de-occlusion without an occlusion mask, enabled by learning in a semi-supervised fashion using (i) paired images with known masks of artificial occlusions and (ii) natural images without occlusion masks. The generator of our approach first predicts an occlusion mask, which is used for filtering the feature maps of the input image as a semantic cue for de-occlusion. The filtered feature maps are then used for face completion to recover a non-occluded face image. The discriminator of our approach consists of an adversarial loss and an attribute preserving loss. Experimental evaluations on the widely used CelebA dataset and a dataset with natural occlusions we collected show that the proposed approach can outperform the state of the art methods in natural face de-occlusion.
J. Cai, H. Han, J. Cui, J. Chen, L. Liu, and S. K. Zhou, “Semi-supervised Natural Face De-occlusion”, IEEE Transactions on Information Forensics and Security, 2020. (Accepted)