Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28340
DC FieldValueLanguage
dc.contributor.authorZhang, Len_US
dc.contributor.authorYang, Men_US
dc.contributor.authorFeng, Xen_US
dc.date.accessioned2015-09-30T09:44:02Z-
dc.date.available2015-09-30T09:44:02Z-
dc.date.issued2011-
dc.identifier.citation2011 IEEE International Conference on Computer Vision (ICCV), 6-13 November 2011, Barcelona, p. 471-478en_US
dc.identifier.isbn978-1-4577-1101-5-
dc.identifier.issn1550-5499-
dc.identifier.urihttp://hdl.handle.net/10397/28340-
dc.description.abstractAs a recently proposed technique, sparse representation based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. While the importance of sparsity is much emphasized in SRC and many related works, the use of collaborative representation (CR) in SRC is ignored by most literature. However, is it really the l1-norm sparsity that improves the FR accuracy? This paper devotes to analyze the working mechanism of SRC, and indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a very simple yet much more efficient face classification scheme, namely CR based classification with regularized least square (CRC_RLS). The extensive experiments clearly show that CRC_RLS has very competitive classification results, while it has significantly less complexity than SRC.en_US
dc.description.sponsorshipDepartment of Computingen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectFace recognitionen_US
dc.subjectImage classificationen_US
dc.subjectImage representationen_US
dc.subjectLeast squares approximationsen_US
dc.titleSparse representation or collaborative representation : which helps face recognition?en_US
dc.typeConference Paperen_US
dc.identifier.spage471-
dc.identifier.epage478-
dc.identifier.doi10.1109/ICCV.2011.6126277-
dc.relation.ispartofbook2011 IEEE International Conference on Computer Vision (ICCV), 6-13 November 2011, Barcelona-
dc.identifier.rosgroupidr59050-
dc.description.ros2011-2012 > Academic research: refereed > Refereed conference paper-
Appears in Collections:Conference Paper
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

SCOPUSTM   
Citations

1,070
Last Week
4
Last month
Citations as of Aug 17, 2018

Page view(s)

1,507
Last Week
65
Last month
Citations as of Aug 13, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.