Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/200378
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dc.contributor.CRUESPUNIVERSIDADE DE ESTADUAL DE CAMPINASpt_BR
dc.typeArtigo de periódicopt_BR
dc.titleFace Identification Using Large Feature Sets.pt_BR
dc.contributor.authorSchwartz, William Robsonpt_BR
dc.contributor.authorGuo, Huiminpt_BR
dc.contributor.authorChoi, Jonghyunpt_BR
dc.contributor.authorDavis, Larry Spt_BR
unicamp.authorWilliam Robson Schwartz, Institute of Computing, University of Campinas, Campinas-SP, Brazil. schwartz@ic.unicamp.brpt_BR
unicamp.author.externalHuimin Guo,pt
unicamp.author.externalJonghyun Choi,pt
unicamp.author.externalLarry S Davis,pt
dc.subjectAlgorithmspt_BR
dc.subjectArtificial Intelligencept_BR
dc.subjectBiometrypt_BR
dc.subjectFacept_BR
dc.subjectHumanspt_BR
dc.subjectImage Enhancementpt_BR
dc.subjectImage Interpretation, Computer-assistedpt_BR
dc.subjectInformation Storage And Retrievalpt_BR
dc.subjectPattern Recognition, Automatedpt_BR
dc.subjectReproducibility Of Resultspt_BR
dc.subjectSensitivity And Specificitypt_BR
dc.subjectSubtraction Techniquept_BR
dc.description.abstractWith the goal of matching unknown faces against a gallery of known people, the face identification task has been studied for several decades. There are very accurate techniques to perform face identification in controlled environments, particularly when large numbers of samples are available for each face. However, face identification under uncontrolled environments or with a lack of training data is still an unsolved problem. We employ a large and rich set of feature descriptors (with more than 70,000 descriptors) for face identification using partial least squares to perform multichannel feature weighting. Then, we extend the method to a tree-based discriminative structure to reduce the time required to evaluate probe samples. The method is evaluated on Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. Experiments show that our identification method outperforms current state-of-the-art results, particularly for identifying faces acquired across varying conditions.en
dc.relation.ispartofIeee Transactions On Image Processing : A Publication Of The Ieee Signal Processing Societypt_BR
dc.relation.ispartofabbreviationIEEE Trans Image Processpt_BR
dc.date.issued2012-Aprpt_BR
dc.identifier.citationIeee Transactions On Image Processing : A Publication Of The Ieee Signal Processing Society. v. 21, n. 4, p. 2245-55, 2012-Apr.pt_BR
dc.language.isoengpt_BR
dc.description.volume21pt_BR
dc.description.firstpage2245-55pt_BR
dc.rightsfechadopt_BR
dc.rights.holderpt_BR
dc.sourcePubMedpt_BR
dc.identifier.issn1941-0042pt_BR
dc.identifier.doi10.1109/TIP.2011.2176951pt_BR
dc.identifier.urlhttp://www.ncbi.nlm.nih.gov/pubmed/22128005pt_BR
dc.date.available2015-11-27T13:29:14Z-
dc.date.accessioned2015-11-27T13:29:14Z-
dc.description.provenanceMade available in DSpace on 2015-11-27T13:29:14Z (GMT). No. of bitstreams: 0 Previous issue date: 2012en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/200378-
dc.identifier.idPubmed22128005pt_BR
Appears in Collections:Unicamp - Artigos e Outros Documentos

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