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dc.contributor.authorZhang, B
dc.contributor.authorGao, Y
dc.date.accessioned2017-05-03T14:13:05Z
dc.date.available2017-05-03T14:13:05Z
dc.date.issued2012
dc.date.modified2013-06-03T04:50:42Z
dc.identifier.issn1755-8301
dc.identifier.doi10.1504/IJBM.2012.044296
dc.identifier.urihttp://hdl.handle.net/10072/47170
dc.description.abstractFace retrieval has received much attention in recent years. This paper comparatively studied five feature description methods for face representation, including Local Binary Pattern (LBP), Gabor feature, Gray Level Co-occurrence Matrices (GLCM), Pyramid Histogram of Oriented Gradient (PHOG) and Curvelet Transform (CT). The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR). A fusion scheme was proposed by aggregating the distance metrics. Experiments illustrated that dimension reduced features are more efficient and the fusion scheme can offer much enhanced performance. A 98% rank 1 accuracy was obtained for the AR faces and 92% for the FERET faces.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInderscience Publishers
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom77
dc.relation.ispartofpageto101
dc.relation.ispartofissue1
dc.relation.ispartofjournalInternational Journal of Biometrics
dc.relation.ispartofvolume4
dc.rights.retentionY
dc.subject.fieldofresearchBiochemistry and cell biology
dc.subject.fieldofresearchComputer vision
dc.subject.fieldofresearchTheory of computation
dc.subject.fieldofresearchcode3101
dc.subject.fieldofresearchcode460304
dc.subject.fieldofresearchcode4613
dc.titleSpectral Regression dimension reduction for multiple features facial image retrieval
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2012
gro.hasfulltextNo Full Text
gro.griffith.authorGao, Yongsheng


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