DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Tae-Kyun | ko |
dc.contributor.author | Arandjelovic, Ognjen | ko |
dc.contributor.author | Cipolla, Roberto | ko |
dc.date.accessioned | 2021-06-17T06:50:41Z | - |
dc.date.available | 2021-06-17T06:50:41Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2007-09 | - |
dc.identifier.citation | PATTERN RECOGNITION, v.40, no.9, pp.2475 - 2484 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10203/285983 | - |
dc.description.abstract | In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Boosted manifold principal angles for image set-based recognition | - |
dc.type | Article | - |
dc.identifier.wosid | 000246932200009 | - |
dc.identifier.scopusid | 2-s2.0-34247572415 | - |
dc.type.rims | ART | - |
dc.citation.volume | 40 | - |
dc.citation.issue | 9 | - |
dc.citation.beginningpage | 2475 | - |
dc.citation.endingpage | 2484 | - |
dc.citation.publicationname | PATTERN RECOGNITION | - |
dc.identifier.doi | 10.1016/j.patcog.2006.12.030 | - |
dc.contributor.localauthor | Kim, Tae-Kyun | - |
dc.contributor.nonIdAuthor | Arandjelovic, Ognjen | - |
dc.contributor.nonIdAuthor | Cipolla, Roberto | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | face recognition | - |
dc.subject.keywordAuthor | manifolds | - |
dc.subject.keywordAuthor | image set | - |
dc.subject.keywordAuthor | principal angle | - |
dc.subject.keywordAuthor | canonical correlation analysis | - |
dc.subject.keywordAuthor | boosting | - |
dc.subject.keywordAuthor | nonlinear subspace | - |
dc.subject.keywordAuthor | illumination | - |
dc.subject.keywordAuthor | pose | - |
dc.subject.keywordAuthor | robustness | - |
dc.subject.keywordAuthor | invariance | - |
dc.subject.keywordPlus | EIGENFACES | - |
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