Open Access System for Information Sharing

Login Library

 

Article
Cited 32 time in webofscience Cited 39 time in scopus
Metadata Downloads
Full metadata record
Files in This Item:
There are no files associated with this item.
DC FieldValueLanguage
dc.contributor.authorKim, HC-
dc.contributor.authorKim, D-
dc.contributor.authorBang, SY-
dc.date.accessioned2016-03-31T12:46:45Z-
dc.date.available2016-03-31T12:46:45Z-
dc.date.created2009-02-28-
dc.date.issued2003-11-
dc.identifier.issn0167-8655-
dc.identifier.other2003-OAK-0000003623-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/18381-
dc.description.abstractLinear discriminant analysis (LDA) provides the projection that discriminates data well, and shows a good performance for face recognition. However, since LDA provides only one transformation matrix over the whole data, it is not sufficient to discriminate complex data consisting of many classes with high variations, such as human faces. To overcome this weakness, we propose a new face recognition method based on the LDA mixture model, where the set of all classes are partitioned into several clusters and we obtain a transformation matrix for each cluster. This accurate and detailed representation will improve classification performance. Simulation results of face recognition show that LDA mixture model outperforms PCA, LDA, and PCA mixture model in terms of classification performance. (C) 2003 Elsevier B.V. All rights reserved.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.relation.isPartOfPATTERN RECOGNITION LETTERS-
dc.subjectlinear discriminant analysis-
dc.subjectLDA mixture model-
dc.subjectPCA mixture model-
dc.subjectface recognition-
dc.subjectEM ALGORITHM-
dc.titleFace recognition using LDA mixture model-
dc.typeArticle-
dc.contributor.college컴퓨터공학과-
dc.identifier.doi10.1016/S0167-8655(03)00126-0-
dc.author.googleKim, HC-
dc.author.googleKim, D-
dc.author.googleBang, SY-
dc.relation.volume24-
dc.relation.issue15-
dc.relation.startpage2815-
dc.relation.lastpage2821-
dc.contributor.id10054411-
dc.relation.journalPATTERN RECOGNITION LETTERS-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCIE-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationPATTERN RECOGNITION LETTERS, v.24, no.15, pp.2815 - 2821-
dc.identifier.wosid000184859600030-
dc.date.tcdate2019-01-01-
dc.citation.endPage2821-
dc.citation.number15-
dc.citation.startPage2815-
dc.citation.titlePATTERN RECOGNITION LETTERS-
dc.citation.volume24-
dc.contributor.affiliatedAuthorKim, D-
dc.identifier.scopusid2-s2.0-0041328237-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc28-
dc.type.docTypeArticle-
dc.subject.keywordAuthorlinear discriminant analysis-
dc.subject.keywordAuthorLDA mixture model-
dc.subject.keywordAuthorPCA mixture model-
dc.subject.keywordAuthorface recognition-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher

김대진KIM, DAI JIN
Dept of Computer Science & Enginrg
Read more

Views & Downloads

Browse