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dc.contributor.authorGao, Yongshengen_US
dc.contributor.authorLiu, Jianzhuangen_US
dc.contributor.authorZhang, Baochangen_US
dc.contributor.authorZhao, Sanqiangen_US
dc.contributor.editorEditor-in-Chief: Thrasos Pappasen_US
dc.date.accessioned2017-04-04T20:44:00Z
dc.date.available2017-04-04T20:44:00Z
dc.date.issued2010en_US
dc.date.modified2010-07-15T09:43:06Z
dc.identifier.issn10577149en_US
dc.identifier.doi10.1109/TIP.2009.2035882en_AU
dc.identifier.urihttp://hdl.handle.net/10072/32176
dc.description.abstractThis paper proposes a novel high-order local pattern descriptor, Local Derivative Pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The nth-order LDP is proposed to encode the (n-1)th-order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in Local Binary Pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent1902167 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEE Signal Processing Societyen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom533en_US
dc.relation.ispartofpageto544en_US
dc.relation.ispartofissue2en_AU
dc.relation.ispartofjournalIEEE Transactions on Image Processingen_US
dc.relation.ispartofvolume19en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.subject.fieldofresearchcode280208en_US
dc.titleLocal Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptoren_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.en_AU
gro.date.issued2010
gro.hasfulltextFull Text


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