Kernel-based subspace analysis for face recognition

Pohsiang Tsai, Tony Jan, Tom Hintz

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

5 Citations (Scopus)

Abstract

In face recognition, if the extracted input data contains misleading information (uncertainty), the classifiers may produce degraded classification performance. In this paper, we employed kernel-based discriminant analysis method for the non-separable problems in face recognition under facial expression changes. The effect of the transformations on a subsequent classification was tested in combination with learning algorithms. We found that the transformation of kernel-based discriminant analysis has a beneficial effect on the classification performance. The experimental results indicated that the nonlinear discriminant analysis method dealt with the uncertainty problem very well. Facial expressions can be used as another behavior biometric for human identification. It appears that face recognition may be robust to facial expression changes, and thus applicable.

Original languageEnglish
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages1127-1132
Number of pages6
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: 12 Aug 200717 Aug 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

Conference2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/TerritoryUnited States
CityOrlando, FL
Period12/08/0717/08/07

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