Face recognition using margin-enhanced classifier in graph-based space

Ju Chin Chen, Shang You Shi, James Jenn-Jier Lien

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we develop a face recognition system with the derived subspace learning method, i.e. classifier-concerning subspace, where not only the discriminant structure of data can be preserved but also the classification ability can be explicitly considered by introducing the Mahalanobis distance metric in the subspace. Most of graph-based subspace learning methods find a subspace with the preservation of certain geometric and discriminant structure of data but not explicitly include the classification information from the classifier. Via the distance metric, which is constrained by k-NN classification rule, the pairwise distance relation can be locally adjusted and thus the projected data in the classifier-concerning subspace are more suitable for k-NN classifier. In addition, an iterative procedure is derived to get rid of the overfitting problem. Experimental results show that the proposed system can yield the promising recognition results under various lighting, pose and expression conditions.

Original languageEnglish
Title of host publicationVISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications
Pages382-388
Number of pages7
Publication statusPublished - 2010 Sep 10
Event5th International Conference on Computer Vision Theory and Applications, VISAPP 2010 - Angers, France
Duration: 2010 May 172010 May 21

Publication series

NameVISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications
Volume2

Other

Other5th International Conference on Computer Vision Theory and Applications, VISAPP 2010
CountryFrance
CityAngers
Period10-05-1710-05-21

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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