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COMBINING CLASSIFIERS FOR FACE RECOGNITION

  • Xiaoguang Lu*
  • , Yunhong Wang
  • , K. Jain*
  • *Corresponding author for this work
  • Michigan State University
  • CAS - Institute of Automation

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

Abstract

Current two-dimensional face recognition approaches can obtain a good performance only under constrained environments. However, in the real applications, face appearance changes significantly due to different illumination, pose, and expression. Face recognizers based on different representations of the input face images have different sensitivity to these variations. Therefore, a combination of different face classifiers which can integrate the complementary information should lead to improved classification accuracy. We use the sum rule and RBF-based integration strategies to combine three commonly used face classifiers based on PCA, ICA and LDA representations. Experiments conducted on a face database containing 206 subjects (2,060 face images) show that the proposed classifier combination approaches outperform individual classifiers.

Original languageEnglish
Title of host publicationProceedings - 2003 International Conference on Multimedia and Expo, ICME
PublisherIEEE Computer Society
PagesIII3-III16
ISBN (Electronic)0780379659
DOIs
StatePublished - 2003
Externally publishedYes
Event2003 International Conference on Multimedia and Expo, ICME 2003 - Baltimore, United States
Duration: 6 Jul 20039 Jul 2003

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume3
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2003 International Conference on Multimedia and Expo, ICME 2003
Country/TerritoryUnited States
CityBaltimore
Period6/07/039/07/03

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