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Mutual Neighborhood Based Discriminant Projection for Face Recognition

  • Ben Niu
  • Simon Chi-Keung Shiu
  • Sankar Pal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

Linear Discriminant Analysis is optimal under the assumption that the covariance matrices of the conditional densities are normal and all identical. However, this doesn’t hold for many real world applications, such as Facial Image Recognition, in which data are typically under-sampled and non-Gaussian. To address this deficiency the Non-Parametric Discriminant method has been developed, but it requires model selection to be carried out for selecting the free control parameters, making it not easy for use in practice. We proposed a method, Mutual Neighborhood based Discriminant Projection, to overcome this problem. MNDP identifies the samples that contribute most to the Baysesian errors and highlights them for optimization. It is more convenient for use than NDA and avoids the singularity problem of LDA. On facial image datasets MNDP is shown to outperform Eigenfaces and Fisherfaces under various experimental conditions.

Keywords

k-nearest neighbors mutual neighborhood discriminant projection face recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ben Niu
    • 1
  • Simon Chi-Keung Shiu
    • 1
  • Sankar Pal
    • 2
  1. 1.Department of ComputingHong Kong Polytechnic UniversityHong KongChina
  2. 2.Indian Statistical InstituteKolkataIndia

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