Robust design of face recognition systems

  • Authors:

  • Sunjin Yu;Hyobin Lee;Jaihie Kim;Sangyoun Lee

  • Affiliations:

  • Graduate Program in Biometrics, and of BERC;Graduate Program in Biometrics, and of BERC;Department of Electrical and Electronic Engineering, and of BERC, Yonsei University, Seoul, Korea;Department of Electrical and Electronic Engineering, and of BERC, Yonsei University, Seoul, Korea

  • Venue:
  • ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
  • Year:
  • 2006

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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.