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dc.contributor.authorChiachia, Giovani [UNESP]
dc.contributor.authorMarana, Aparecido Nilceu [UNESP]
dc.contributor.authorRuf, Tobias
dc.contributor.authorErnst, Andreas
dc.date.accessioned2014-05-20T13:25:58Z
dc.date.available2014-05-20T13:25:58Z
dc.date.issued2011-12-01
dc.identifierhttp://dx.doi.org/10.1142/S0218001411009068
dc.identifier.citationInternational Journal of Pattern Recognition and Artificial Intelligence. Singapore: World Scientific Publ Co Pte Ltd, v. 25, n. 8, p. 1337-1348, 2011.
dc.identifier.issn0218-0014
dc.identifier.urihttp://hdl.handle.net/11449/8301
dc.description.abstractMost face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance.en
dc.format.extent1337-1348
dc.language.isoeng
dc.publisherWorld Scientific Publ Co Pte Ltd
dc.relation.ispartofInternational Journal of Pattern Recognition and Artificial Intelligence
dc.sourceWeb of Science
dc.subjectFace recognitionen
dc.subjectcensus transformen
dc.subjectlocal binary patternsen
dc.subjecthistogram matchingen
dc.subjectfeature extractionen
dc.titleCENSUS HISTOGRAMS: A SIMPLE FEATURE EXTRACTION and MATCHING APPROACH FOR FACE RECOGNITIONen
dc.typeArtigo
dcterms.licensehttp://www.worldscientific.com/page/authors/author-rights
dcterms.rightsHolderWorld Scientific Publ Co Pte Ltd
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionFraunhofer Inst Integrated Circuits IIS
dc.description.affiliationSão Paulo State Univ, Dept Comp, BR-17033360 São Paulo, Brazil
dc.description.affiliationFraunhofer Inst Integrated Circuits IIS, Elect Imaging Dept, D-91058 Erlangen, Germany
dc.description.affiliationUnespSão Paulo State Univ, Dept Comp, BR-17033360 São Paulo, Brazil
dc.identifier.doi10.1142/S0218001411009068
dc.identifier.wosWOS:000298813200010
dc.rights.accessRightsAcesso restrito
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
dc.identifier.lattes6027713750942689
unesp.author.lattes6027713750942689[2]
unesp.author.orcid0000-0003-4861-7061[2]
dc.relation.ispartofjcr1.029
dc.relation.ispartofsjr0,315
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