Joint dynamic sparse representation for multi-view face recognition

Haichao Zhang, Nasser M. Nasrabadi, Yanning Zhang, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the problem of automatically recognizing a human face from its multi-view images with unconstrained poses. We formulate the multi-view face recognition task as a joint sparse representation model and take advantage of the correlations among the multiple views for face recognition using a novel joint dynamic sparsity prior. The proposed joint dynamic sparsity prior promotes shared joint sparsity patterns among the multiple sparse representation vectors at class-level, while allowing distinct sparsity patterns at atom-level within each class to facilitate a flexible representation. Extensive experiments on the CMU Multi-PIE face database are conducted to verify the efficacy of the proposed method.

Original languageEnglish (US)
Pages (from-to)1290-1298
Number of pages9
JournalPattern Recognition
Volume45
Issue number4
DOIs
StatePublished - Apr 2012

Keywords

  • Joint dynamic sparse representation based classification
  • Joint dynamic sparsity
  • Multi-view face recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Joint dynamic sparse representation for multi-view face recognition'. Together they form a unique fingerprint.

Cite this