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

查看全文 作　　者：Jianquan [1,2]Gu;Haifeng [1,2]Hu;Haoxi [1,2]Li

机构地区：[1]School of Electronics and Information Technology, Sun Yat-sen University (SYSU), Guangzhou 510275, China;[2]SYSU-CMU Shunde International Joint Research Institute, Shunde 528300, China

出　　处：《IEEE/CAA Journal of Automatica Sinica》索引2018年第5卷第2期,共8页

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 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 images.The 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. 关键词：Dictionary learning face recognition(FR) illumination changes single sample per person(SSPP) sparse representation

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

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