Tuning kernel parameters with different gabor features for face recognition

Linlin Shen, Zhen Ji, Li Bai

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


Kernel methods like support vector machine, kernel principal component analysis and kernel fisher discriminant analysis have recently been successfully applied to solve pattern recognition problems such as face recognition. However, most of the papers present the results without giving kernel parameters, or giving parameters without any explains. In this paper, we present an experiments based approach to optimize the performance of a Gabor feature and kernel method based face recognition system. During the process of parameter tuning, the robustness of the system against variations of kernel function, kernel parameters and Gabor features are extensively tested. The results suggest that the kernel method based approach, with tuned parameters, achieves significantly better results than other algorithms available in literature.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Subtitle of host publicationWith Aspects of Artificial Intelligence - Third International Conference on Intelligent Computing, ICIC 2007, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783540742012
Publication statusPublished - 2007
Externally publishedYes
Event3rd International Conference on Intelligent Computing, ICIC 2007 - Qingdao, China
Duration: 21 Aug 200724 Aug 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4682 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Intelligent Computing, ICIC 2007


  • Gabor features
  • Kernel methods

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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