Low-Resolution Face Recognition

被引：25

作者：

Cheng, Zhiyi ^{[1
]}

Zhu, Xiatian ^{[2
]}

Gong, Shaogang ^{[1
]}

机构：

[1] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England

[2] Vis Semant Ltd, London, England

来源：

COMPUTER VISION - ACCV 2018, PT III | 2019年 / 11363卷

基金：

“创新英国”项目;

关键词：

Face recognition; Low-resolution; Super-resolution; SUPERRESOLUTION;

D O I：

10.1007/978-3-030-20893-6_38

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Whilst recent face-recognition (FR) techniques have made significant progress on recognising constrained high-resolution web images, the same cannot be said on natively unconstrained low-resolution images at large scales. In this work, we examine systematically this under-studied FR problem, and introduce a novel Complement Super-Resolution and Identity (CSRI) joint deep learning method with a unified end-to-end network architecture. We further construct a new large-scale dataset TinyFace of native unconstrained low-resolution face images from selected public datasets, because none benchmark of this nature exists in the literature. With extensive experiments we show there is a significant gap between the reported FR performances on popular benchmarks and the results on TinyFace, and the advantages of the proposed CSRI over a variety of state-of-the-art FR and super-resolution deep models on solving this largely ignored FR scenario. The TinyFace dataset is released publicly at: https://qmul-tinyface.github.io/.

引用

页码：605 / 621

页数：17