  In this paper, we address the problem of face hallucination by proposing a
novel multi-scale generative adversarial network (GAN) architecture optimized
for face verification. First, we propose a multi-scale generator architecture
for face hallucination with a high up-scaling ratio factor, which has multiple
intermediate outputs at different resolutions. The intermediate outputs have
the growing goal of synthesizing small to large images. Second, we incorporate
a face verifier with the original GAN discriminator and propose a novel
discriminator which learns to discriminate different identities while
distinguishing fake generated HR face images from their ground truth images. In
particular, the learned generator cares for not only the visual quality of
hallucinated face images but also preserving the discriminative features in the
hallucination process. In addition, to capture perceptually relevant
differences we employ a perceptual similarity loss, instead of similarity in
pixel space. We perform a quantitative and qualitative evaluation of our
framework on the LFW and CelebA datasets. The experimental results show the
advantages of our proposed method against the state-of-the-art methods on the
8x downsampled testing dataset.
