Open AccessSurvey on GAN‐based face hallucination with its model development
Author(s) -
Liu Heng,
Zheng Xiaoyu,
Han Jungong,
Chu Yuezhong,
Tao Tao
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6545
Subject(s) - face hallucination , face (sociological concept) , computer science , artificial intelligence , computer vision , image (mathematics) , task (project management) , generative adversarial network , superresolution , facial recognition system , pattern recognition (psychology) , face detection , linguistics , engineering , philosophy , systems engineering
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.