{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:48:21Z","timestamp":1761648501766},"reference-count":21,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2018,3]]},"abstract":"<jats:p> For face recognition, conventional dictionary learning (DL) methods have some disadvantages. First, face images of the same person vary with facial expressions and pose, illumination and disguises, so it is hard to obtain a robust dictionary for face recognition. Second, they don\u2019t cover important components (e.g., particularity and disturbance) completely, which limit their performance. In the paper, we propose a novel robust and discriminative DL (RDDL) model. The proposed model uses sample diversities of the same face image to learn a robust dictionary, which includes class-specific dictionary atoms and disturbance dictionary atoms. These atoms can well represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which improves effectively the classification capability of the dictionary. The proposed RDDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition. <\/jats:p>","DOI":"10.1142\/s0219691318400040","type":"journal-article","created":{"date-parts":[[2018,1,4]],"date-time":"2018-01-04T09:22:32Z","timestamp":1515057752000},"page":"1840004","source":"Crossref","is-referenced-by-count":5,"title":["Robust and discriminative dictionary learning for face recognition"],"prefix":"10.1142","volume":"16","author":[{"given":"Guojun","family":"Lin","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, P. R. China"}]},{"given":"Meng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, P. R. China"}]},{"given":"Linlin","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, P. R. China"}]},{"given":"Mingzhong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, P. R. China"}]},{"given":"Mei","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. 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