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Kernel relevance weighted discriminant analysis for face recognition

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Abstract

In this paper, we propose a new kernel discriminant analysis called kernel relevance weighted discriminant analysis (KRWDA) which has several interesting characteristics. First, it can effectively deal with the small sample size problem by using a QR decomposition on scatter matrices. Second, by incorporating a weighting function into discriminant criterion, it overcomes overemphasis on well-separated classes and hence can work under more realistic situations. Finally, using kernel theory, it handle non linearity efficiently. In order to improve performance of the proposed algorithm, we introduce two novel kernel functions and compare them with some commonly used kernels on face recognition field. We have performed multiple face recognition experiments to compare KRWDA with other dimensionality reduction methods showing that KRWDA consistently gives the best results.

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Notes

  1. Kernels parameters: polynomial (d = 2), fractional power polynomial (d = 0.4), Gaussian kernel (σ2 = 105), power distance and logarithmic kernels with (β = 2).

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Acknowledgements

The authors wish to thank the anonymous reviewers, whose comments were a great help to raise the level of technical and formal correctness of this paper.

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Authors and Affiliations

  1. Laboratory of Conception and Systems, Faculty of Science Agdal, Mohammed V University, Avenue Ibn Battouta, B.P. 1014, Rabat, Morocco

    Khalid Chougdali, Mohamed Jedra & Nouredine Zahid

Authors
  1. Khalid Chougdali
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  2. Mohamed Jedra
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  3. Nouredine Zahid
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Corresponding author

Correspondence to Khalid Chougdali.

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Chougdali, K., Jedra, M. & Zahid, N. Kernel relevance weighted discriminant analysis for face recognition. Pattern Anal Applic 13, 213–221 (2010). https://doi.org/10.1007/s10044-009-0152-3

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  • DOI: https://doi.org/10.1007/s10044-009-0152-3

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