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Collaborative representation based projections for face recognition

  • Southeast University, Nanjing
  • University of North Carolina at Wilmington

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In this paper, we develop a collaborative representation based projections (CRP) for face recognition, which is an unsupervised method. Like SPP and NPE, CRP aims to preserve the sparse reconstruction relations of data. CRP is much faster than SPP since CRP adopts collaborative representation with regularized least square related as objective function while SPP adopts sparse representation related as objective function. Experimental results on ORL and FERET demonstrate that CRP works well in feature extraction and leads to good recognition performance.

Original languageEnglish
Title of host publicationPattern Recognition - Chinese Conference, CCPR 2012, Proceedings
Pages276-283
Number of pages8
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 5th Chinese Conference on Pattern Recognition, CCPR 2012 - Beijing, China
Duration: 24 Sep 201226 Sep 2012

Publication series

NameCommunications in Computer and Information Science
Volume321 CCIS
ISSN (Print)1865-0929

Conference

Conference2012 5th Chinese Conference on Pattern Recognition, CCPR 2012
Country/TerritoryChina
CityBeijing
Period24/09/1226/09/12

Keywords

  • collaborative representation
  • face recognition
  • feature extraction

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