Face Video Super-Resolution with Identity Guided Generative Adversarial Networks

被引：5

作者：

Li, Dingyi ^{[1
]}

Wang, Zengfu ^{[1
,2
]}

机构：

[1] Univ Sci & Technol China, Dept Automat, Hefei 230027, Anhui, Peoples R China

[2] Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China

来源：

COMPUTER VISION, PT II | 2017年 / 772卷

基金：

中国国家自然科学基金;

关键词：

Super-resolution; Face hallucination; Identity guidance; Generative adversarial networks (GANs); HALLUCINATION;

D O I：

10.1007/978-981-10-7302-1_30

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Faces are of particular concerns in video surveillance systems. It is challenging to reconstruct clear faces from low-resolution (LR) videos. In this paper, we propose a new method for face video super-resolution (SR) based on identity guided generative adversarial networks (GANs). We establish a two-stage convolutional neural network (CNN) for face video SR, and employ identity guided GANs to recover high-resolution (HR) facial details. Extensive experiments validate the effectiveness of our proposed method from the following aspects: fidelity, visual quality and robustness to pose, expression and illuminance variations.

引用

页码：357 / 369

页数：13

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