Sparse representation with nearest subspaces for face recognition

Jinghua Wang, Jia You, Qin Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

This paper proposes a supervised sparse representation based classification method for face recognition. The proposed method consists of two phases. The first phase seeks k nearest subspaces for the test sample from the c classes, and converts the c -class classification problem into a k -class problem, where c is the number of classes and k is a parameter. We can do this because, as validated by the experiments, the test sample belongs to one of these k subspaces with a very high probability. The second phase represents the test sample using these k nearest subspaces. This phase enhances the sparability by increasing the contribution of right subspace and suppressing those of the others. Via the above two phases, the proposed method introduces an approach to gain the sparsity in representation. The experimental results show the feasibility of the proposed method in face recognition.
Original languageEnglish
Title of host publicationProceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Pages567-572
Number of pages6
Volume2
Publication statusPublished - 1 Dec 2012
Event2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012 - Las Vegas, NV, United States
Duration: 16 Jul 201219 Jul 2012

Conference

Conference2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Country/TerritoryUnited States
CityLas Vegas, NV
Period16/07/1219/07/12

Keywords

  • Face recognition
  • Feature extraction
  • Nearest subspace
  • Sparse representation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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