Survey on GAN‐based face hallucination with its model development. Issue 14 (1st December 2019)

• Record Type: Journal Article
• Authors: Liu, Heng; Zheng, Xiaoyu; Han, Jungong; Chu, Yuezhong; Tao, Tao
• Is Part Of: IET image processing. Volume 13:Issue 14(2019)
• Page Start: 2662
• Page End: 2672
• Publication Date: 2019-12-01
• Subjects: image resolution -- face recognition -- learning (artificial intelligence); high‐resolution face image -- input low‐resolution face image -- practical face applications -- face verification -- super‐resolved face image -- generic image super‐resolution -- high‐level face recognition task -- image resolution degradation -- GAN‐based learning -- state‐of‐art GAN‐based face hallucination; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6545
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Face hallucination aims to produce a high‐resolution face image from an input low‐resolution face image, which is of great importance for many practical face applications, such as face recognition and face verification. Since the structure of the face image is complex and sensitive, obtaining a super‐resolved face image is more difficult than generic image super‐resolution. Recently, with great success in the high‐level face recognition task, deep learning methods, especially generative adversarial networks (GANs), have also been applied to the low‐level vision task – face hallucination. This work is to provide a model evolvement survey on GAN‐based face hallucination. The principles of image resolution degradation and GAN‐based learning are presented firstly. Then, a comprehensive review of the state‐of‐art GAN‐based face hallucination methods is provided. Finally, the comparisons of these GAN‐based face hallucination methods and the discussions of the related issues for future research direction are also provided.