  Combined variations containing low-resolution and occlusion often present in
face images in the wild, e.g., under the scenario of video surveillance. While
most of the existing face image recovery approaches can handle only one type of
variation per model, in this work, we propose a deep generative adversarial
network (FCSR-GAN) for performing joint face completion and face
super-resolution via multi-task learning. The generator of FCSR-GAN aims to
recover a high-resolution face image without occlusion given an input
low-resolution face image with occlusion. The discriminator of FCSR-GAN uses a
set of carefully designed losses (an adversarial loss, a perceptual loss, a
pixel loss, a smooth loss, a style loss, and a face prior loss) to assure the
high quality of the recovered high-resolution face images without occlusion.
The whole network of FCSR-GAN can be trained end-to-end using our two-stage
training strategy. Experimental results on the public-domain CelebA and Helen
databases show that the proposed approach outperforms the state-of-the-art
methods in jointly performing face super-resolution (up to 8 $\times$) and face
completion, and shows good generalization ability in cross-database testing.
Our FCSR-GAN is also useful for improving face identification performance when
there are low-resolution and occlusion in face images.
