Local Robust Sparse Representation for Face Recognition With Single Sample per Person

作     者：Jianquan Gu Haifeng Hu Haoxi Li 

作者机构：School of Electronics and Information Technology Sun Yat-sen University (SYSU) Guangzhou 510275 China SYSU-CMU Shunde International Joint Research Institute Shunde 528300 China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2018年第5卷第2期

页      面：547-554页

核心收录：

学科分类：08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 

基　　金：supported in part by the National Natural Science Foundation of China(61673402,61273270,60802069) the Natural Science Foundation of Guangdong Province(2017A030311029,2016B010109002,2015B090912001,2016B010123005,2017B090909005) the Science and Technology Program of Guangzhou of China(201704020180,201604020024) the Fundamental Research Funds for the Central Universities of China 

主　　题：Index Terms-Dictionary learning face recognition (FR) il-lumination changes single sample per person (SSPP) sparserepresentation. 

摘      要：The purpose of this paper is to solve the problem of robust face recognition(FR) with single sample per person(SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation(LRSR) to tackle the problem of query images with various intra-class variations,e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query *** key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intraclass variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.