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Article: Face recognition - a generalized marginal fisher analysis approach

TitleFace recognition - a generalized marginal fisher analysis approach
Authors
Xu, DongTao, DachengLi, XuelongYan, Shuicheng
KeywordsBiometrics
face recognition
Issue Date2007
Citation
International Journal of Image and Graphics, 2007, v. 7, n. 3, p. 583-591 How to Cite?
DOI: http://dx.doi.org/10.1142/S0219467807002817
AbstractIn this paper, we propose a new supervised learning algorithm, which is named the Generalized Marginal Fisher Analysis (GMFA), to utilize the advantages of the Marginal Fisher Analysis (MFA) and the Generalized Singular Value Decomposition (GSVD) techniques for face recognition. The experimental results on several standard face databases demonstrate that GMFA outperforms LDA/Fisherface, LDA/GSVD and MFA.
Persistent Identifierhttp://hdl.handle.net/10722/321585
ISSN
0219-4678
2023 Impact Factor: 0.8
2023 SCImago Journal Rankings: 0.247
ISI Accession Number ID
WOS:000216792700010

 

DC FieldValueLanguage
dc.contributor.authorXu, Dong-
dc.contributor.authorTao, Dacheng-
dc.contributor.authorLi, Xuelong-
dc.contributor.authorYan, Shuicheng-
dc.date.accessioned2022-11-03T02:20:02Z-
dc.date.available2022-11-03T02:20:02Z-
dc.date.issued2007-
dc.identifier.citationInternational Journal of Image and Graphics, 2007, v. 7, n. 3, p. 583-591-
dc.identifier.issn0219-4678-
dc.identifier.urihttp://hdl.handle.net/10722/321585-
dc.description.abstractIn this paper, we propose a new supervised learning algorithm, which is named the Generalized Marginal Fisher Analysis (GMFA), to utilize the advantages of the Marginal Fisher Analysis (MFA) and the Generalized Singular Value Decomposition (GSVD) techniques for face recognition. The experimental results on several standard face databases demonstrate that GMFA outperforms LDA/Fisherface, LDA/GSVD and MFA.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Image and Graphics-
dc.subjectBiometrics-
dc.subjectface recognition-
dc.titleFace recognition - a generalized marginal fisher analysis approach-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1142/S0219467807002817-
dc.identifier.scopuseid_2-s2.0-84900268852-
dc.identifier.volume7-
dc.identifier.issue3-
dc.identifier.spage583-
dc.identifier.epage591-
dc.identifier.isiWOS:000216792700010-

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