DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, HC | - |
dc.contributor.author | Kim, D | - |
dc.contributor.author | Bang, SY | - |
dc.date.accessioned | 2016-03-31T12:46:45Z | - |
dc.date.available | 2016-03-31T12:46:45Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2003-11 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.other | 2003-OAK-0000003623 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/18381 | - |
dc.description.abstract | Linear 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.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | PATTERN RECOGNITION LETTERS | - |
dc.subject | linear discriminant analysis | - |
dc.subject | LDA mixture model | - |
dc.subject | PCA mixture model | - |
dc.subject | face recognition | - |
dc.subject | EM ALGORITHM | - |
dc.title | Face recognition using LDA mixture model | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1016/S0167-8655(03)00126-0 | - |
dc.author.google | Kim, HC | - |
dc.author.google | Kim, D | - |
dc.author.google | Bang, SY | - |
dc.relation.volume | 24 | - |
dc.relation.issue | 15 | - |
dc.relation.startpage | 2815 | - |
dc.relation.lastpage | 2821 | - |
dc.contributor.id | 10054411 | - |
dc.relation.journal | PATTERN RECOGNITION LETTERS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION LETTERS, v.24, no.15, pp.2815 - 2821 | - |
dc.identifier.wosid | 000184859600030 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 2821 | - |
dc.citation.number | 15 | - |
dc.citation.startPage | 2815 | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 24 | - |
dc.contributor.affiliatedAuthor | Kim, D | - |
dc.identifier.scopusid | 2-s2.0-0041328237 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 28 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | linear discriminant analysis | - |
dc.subject.keywordAuthor | LDA mixture model | - |
dc.subject.keywordAuthor | PCA mixture model | - |
dc.subject.keywordAuthor | face recognition | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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