Identity-Aware Deep Face Hallucination via Adversarial Face Verification
Hadi Kazemi, Fariborz Taherkhani, Nasser M. Nasrabadi

TL;DR
This paper introduces a multi-scale GAN architecture for face hallucination that emphasizes identity preservation and perceptual quality, outperforming existing methods on standard datasets.
Contribution
The work presents a novel multi-scale generator and a discriminator with identity verification, enhancing face hallucination with identity preservation and perceptual loss.
Findings
Outperforms state-of-the-art methods on LFW and CelebA datasets.
Effectively preserves identity features in high-resolution face images.
Achieves high-quality face hallucination at 8x upscaling.
Abstract
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…
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Taxonomy
MethodsConvolution · Dogecoin Customer Service Number +1-833-534-1729
