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dc.contributor.authorZhang, Baochangen_US
dc.contributor.authorGao, Yongshengen_US
dc.contributor.authorZheng, Hongen_US
dc.date.accessioned2017-05-03T14:51:51Z
dc.date.available2017-05-03T14:51:51Z
dc.date.issued2011en_US
dc.date.modified2011-08-09T06:27:38Z
dc.identifier.issn09252312en_US
dc.identifier.doi10.1016/j.neucom.2010.09.008en_AU
dc.identifier.urihttp://hdl.handle.net/10072/39788
dc.description.abstractThis paper proposes a new Local Kernel Feature Analysis (LKFA) method for object recognition. LKFA captures the nonlinear local relationship in an image via kernel functions. Different from traditional kernel methods for object recognition, the proposed method does not need to reserve the training samples. LKFA is designed to extract the eigenvalue features from the Hermite matrix of a local feature representation, which we have theoretically proven its robustness to noise and perturbations. Experiment results on palmprint and face recognitions demonstrated the effectiveness of the proposed LKFA that significantly improved the performance of the local feature based object recognition method.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevier BVen_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom575en_US
dc.relation.ispartofpageto579en_US
dc.relation.ispartofissue4en_US
dc.relation.ispartofjournalNeurocomputingen_US
dc.relation.ispartofvolume74en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleLocal Kernel Feature Analysis (LKFA) for object recognitionen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2011
gro.hasfulltextNo Full Text


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