Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15500
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorYang, M-
dc.creatorZhang, L-
dc.creatorYang, J-
dc.creatorZhang, D-
dc.date.accessioned2014-12-31T08:01:20Z-
dc.date.available2014-12-31T08:01:20Z-
dc.identifier.isbn9781424479948-
dc.identifier.issn1522-4880-
dc.identifier.urihttp://hdl.handle.net/10397/15500-
dc.description2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010en_US
dc.language.isoenen_US
dc.subjectFace recognitionen_US
dc.subjectMetaface learningen_US
dc.subjectSparse representationen_US
dc.titleMetaface learning for sparse representation based face recognitionen_US
dc.typeConference Paperen_US
dc.identifier.spage1601-
dc.identifier.epage1604-
dc.identifier.doi10.1109/ICIP.2010.5652363-
dcterms.abstractFace recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the metaface learning (MFL) of face images under the framework of SRC. Although directly using the training samples as dictionary bases can achieve good FR performance, a well learned dictionary matrix can lead to higher FR rate with less dictionary atoms. An SRC oriented unsupervised MFL algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed MFL algorithm over original SRC.-
dcterms.bibliographicCitationProceedings - International Conference on Image Processing, ICIP, 2010, p. 1601-1604-
dcterms.issued2010-
dc.identifier.isiWOS:000287728001173-
dc.identifier.scopus2-s2.0-78651086227-
dc.relation.ispartofbookProceedings - International Conference on Image Processing, ICIP, 2010-
dc.relation.conferenceIEEE International Conference on Image Processing [ICIP]-
dc.identifier.rosgroupidr51248-
dc.description.ros2010-2011 > Academic research: refereed > Refereed conference paper-
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