HUBER COLLABORATIVE REPRESENTATION FOR ROBUST FACE IDENTIFICATION

被引：0

作者：

Wang, Yu-Long ^{[1
]}

Zou, Cui-Mind ^{[1
]}

Tang, Yuan Yan ^{[2
]}

Yang, Li-Na ^{[3
]}

机构：

[1] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China

[2] Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China

[3] Guangxi Univ, Sch Comp Elect & Informat, Nanning, Peoples R China

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION (ICWAPR) | 2018年

基金：

中国国家自然科学基金;

关键词：

Face recognition; Collaborative representation; M-estimation; RECOGNITION;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, we consider the problem of recognizing human faces from the facial images of front views against random corruption. To this end, we develop a Huber collaborative representation based classification (HCRC) approach and apply it to face identification. To handle gross corruption, we exploit the robust Huber estimator as the cost function. A half-quadratic optimization algorithm is devised to solve the HCRC model efficiently. The experiments on real-life data validate the efficacy of HCRC for face identification.

引用

页码：78 / 81

页数：4

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