A robust sparse representation algorithm based on adaptive joint dictionary

作     者：Ying Tong Rui Chen Minghu Wu Yang Jiao 

作者机构：College of Information and Communication EngineeringNanjing Institute of TechnologyNanjingChina College of Electrical and Electronic EngineeringHubei University of TechnologyWuhanChina Department of StatisticsUniversity of TorontoTorontoOntarioCanada 

出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)

年 卷 期：2023年第8卷第2期

页      面：430-439页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

基　　金：Natural Science Foundation of Jiangsu Province,Grant/Award Number:BK20170765 Natural Science Foundation of China,Grant/Award Number:61703201 Science Foundation of Nanjing Institute of Technology,Grant/Award Numbers:ZKJ202002,ZKJ202003,and YKJ202019 

主　　题：facial recognition feature extraction noise dictionary robust regression 

摘      要：Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face *** at the shortcomings of face feature dictionary not‘clean’and noise interference dictionary not‘representative’in sparse representation classification model,a new method named as robust sparse representation is proposed based on adaptive joint dictionary(RSR-AJD).First,a fast lowrank subspace recovery algorithm based on LogDet function(Fast LRSR-LogDet)is proposed for accurate low-rank facial intrinsic dictionary representing the similar structure of human face and low computational ***,the Iteratively Reweighted Robust Principal Component Analysis(IRRPCA)algorithm is used to get a more precise occlusion dictionary for depicting the possible discontinuous interference information attached to human face such as glasses occlusion or scarf occlusion ***,the above Fast LRSR-LogDet algorithm and IRRPCA algorithm are adopted to construct the adaptive joint dictionary,which includes the low-rank facial intrinsic dictionary,the occlusion dictionary and the remaining intra-class variant dictionary for robust sparse *** conducted on four popular databases(AR,Extended Yale B,LFW,and Pubfig)verify the robustness and effectiveness of the authors’method.