Robust design of face recognition systems

Sunjin Yu, Hyobin Lee, Jaihie Kim, Sangyoun Lee

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


Currently, most face recognition methods provide a number of parameters to be optimized, leaving the selection and optimization of the right parameter set is necessary for the implementation. The choice of the right parameter set that is suitable for a rich enough class of input faces in pose and illumination variations is, however, quite difficult. We propose robust parameter estimation, using the Taguchi method, when applied to 2nd order mixture of eigenfaces method that allows effective (near optimal) performance under pose and illumination variations. A number of experimental results confirm the improvement (via robustness) vis-'a-vis conventional parameter estimation methods, and these methods promise a solution to the design of efficient parameter sets that support many multi-variable face recognition systems.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - ICCSA 2006
Subtitle of host publicationInternational Conference, Proceedings - Part II
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540340726, 9783540340720
Publication statusPublished - 2006
EventICCSA 2006: International Conference on Computational Science and Its Applications - Glasgow, United Kingdom
Duration: 2006 May 82006 May 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3981 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherICCSA 2006: International Conference on Computational Science and Its Applications
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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