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Robust Design of Face Recognition Systems

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Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3981))

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Abstract

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.

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References

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Author information

Authors and Affiliations

  1. Graduate Program in Biometrics, and of BERC,  

    Sunjin Yu & Hyobin Lee

  2. Department of Electrical and Electronic Engineering, and of BERC, Yonsei University, 134, Sinchon-dong Seodaemun-gu, Seoul, 120-749, Korea

    Jaihie Kim & Sangyoun Lee

Authors
  1. Sunjin Yu
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  2. Hyobin Lee
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  3. Jaihie Kim
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  4. Sangyoun Lee
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

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© 2006 Springer-Verlag Berlin Heidelberg

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Yu, S., Lee, H., Kim, J., Lee, S. (2006). Robust Design of Face Recognition Systems. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751588_11

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  • DOI: https://doi.org/10.1007/11751588_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34072-0

  • Online ISBN: 978-3-540-34074-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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