Linear collaborative discriminant regression classification for face recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Qu, Xiaochao | - |
dc.contributor.author | Kim, Suah | - |
dc.contributor.author | Cui, Run | - |
dc.contributor.author | Kim, Hyoung Joong | - |
dc.date.accessioned | 2021-09-04T13:50:20Z | - |
dc.date.available | 2021-09-04T13:50:20Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2015-08 | - |
dc.identifier.issn | 1047-3203 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/92860 | - |
dc.description.abstract | This paper proposes a novel face recognition method that improves Huang's linear discriminant regression classification (LDRC) algorithm. The original work finds a discriminant subspace by maximizing the between-class reconstruction error and minimizing the within-class reconstruction error simultaneously, where the reconstruction error is obtained using Linear Regression Classification (LRC). However, the maximization of the overall between-class reconstruction error is easily dominated by some large class-specific between-class reconstruction errors, which makes the following LRC erroneous. This paper adopts a better between-class reconstruction error measurement which is obtained using the collaborative representation instead of class-specific representation and can be regarded as the lower bound of all the class-specific between-class reconstruction errors. Therefore, the maximization of the collaborative between-class reconstruction error maximizes each class-specific between-class reconstruction and emphasizes the small class-specific between-class reconstruction errors, which is beneficial for the following LRC. Extensive experiments are conducted and the effectiveness of the proposed method is verified. (C) 2015 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
dc.subject | REPRESENTATION | - |
dc.title | Linear collaborative discriminant regression classification for face recognition | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Suah | - |
dc.contributor.affiliatedAuthor | Kim, Hyoung Joong | - |
dc.identifier.doi | 10.1016/j.jvcir.2015.07.009 | - |
dc.identifier.scopusid | 2-s2.0-84937808290 | - |
dc.identifier.wosid | 000359181600028 | - |
dc.identifier.bibliographicCitation | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v.31, pp.312 - 319 | - |
dc.relation.isPartOf | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION | - |
dc.citation.title | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION | - |
dc.citation.volume | 31 | - |
dc.citation.startPage | 312 | - |
dc.citation.endPage | 319 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | REPRESENTATION | - |
dc.subject.keywordAuthor | Face recognition | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Dimensionality reduction | - |
dc.subject.keywordAuthor | Collaborative representation | - |
dc.subject.keywordAuthor | Sparse representation | - |
dc.subject.keywordAuthor | Linear regression classification | - |
dc.subject.keywordAuthor | Linear collaborative discriminant regression classification | - |
dc.subject.keywordAuthor | Linear discriminant regression classification | - |
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