SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination.

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ID: 1835
2019
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Ranked #9 of 51 articles by views in ieee transactions on image processing : a publication of the ieee signal processing society

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Abstract
Despite generative adversarial networks (GANs) can hallucinate promising-quality 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 not only achieve visually-pleasant face reconstruction, but also ensure that 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 promising visualquality reconstruction. Moreover, for input LR faces from unseen identities who are not included in training, SiGAN can still do a good job.
Reference Key
hsu2019siganieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hsu, Chih-Chung;Lin, Chia-Wen;Su, Weng-Tai;Cheung, Gene;
Journal ieee transactions on image processing : a publication of the ieee signal processing society
Year 2019
DOI
10.1109/TIP.2019.2924554
URL
Keywords Keywords not found

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