Show simple item record

dc.contributor.authorZhang, Bailingen_US
dc.contributor.authorGao, Yongshengen_US
dc.date.accessioned2017-05-03T14:13:05Z
dc.date.available2017-05-03T14:13:05Z
dc.date.issued2012en_US
dc.date.modified2013-06-03T04:50:42Z
dc.identifier.issn17558301en_US
dc.identifier.doi10.1504/IJBM.2012.044296en_US
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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherInderscience Publishersen_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom77en_US
dc.relation.ispartofpageto101en_US
dc.relation.ispartofissue1en_US
dc.relation.ispartofjournalInternational Journal of Biometricsen_US
dc.relation.ispartofvolume4en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchComputer Visionen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080104en_US
dc.subject.fieldofresearchcode080109en_US
dc.titleSpectral Regression dimension reduction for multiple features facial image retrievalen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2012
gro.hasfulltextNo Full Text


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record