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.

提出了一种基于 Siamese 网络的生成对抗网络（SiGAN）来重建视觉上类似于其对应身份的高分辨率人脸，包括重建误差和身份标签信息，并在 SiGAN 的生成器对和鉴别器中迭代优化，实现了照片般逼真的面部重建和身份识别。实验结果表明，SiGAN 在客观面部验证性能上明显优于现有的人脸幻觉 GAN，并实现了照片般逼真的重建。此外，对于未包含在训练中的未知身份输入 LR 面孔，SiGAN 仍然可以做得很好。