  Despite generative adversarial networks (GANs) can hallucinate
photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they
cannot guarantee preserving the identities of hallucinated HR faces, making the
HR faces poorly recognizable. To address this problem, we propose a Siamese GAN
(SiGAN) to reconstruct HR faces that visually resemble their corresponding
identities. On top of a Siamese network, the proposed SiGAN consists of a pair
of two identical generators and one discriminator. We incorporate
reconstruction error and identity label information in the loss function of
SiGAN in a pairwise manner. By iteratively optimizing the loss functions of the
generator pair and discriminator of SiGAN, we cannot only achieve
photo-realistic face reconstruction, but also ensures the reconstructed
information is useful for identity recognition. Experimental results
demonstrate that SiGAN significantly outperforms existing face hallucination
GANs in objective face verification performance, while achieving
photo-realistic reconstruction. Moreover, for input LR faces from unknown
identities who are not included in training, SiGAN can still do a good job.
