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Semi-supervised Neighborhood Preserving Discriminant Embedding: A Semi-supervised Subspace Learning Algorithm

  • Maryam Mehdizadeh
  • Cara MacNish
  • R. Nazim Khan
  • Mohammed Bennamoun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

Over the last decade, supervised and unsupervised subspace learning methods, such as LDA and NPE, have been applied for face recognition. In real life applications, besides unlabeled image data, prior knowledge in the form of labeled data is also available, and can be incorporated in subspace learning algorithm resulting in improved performance. In this paper, we propose a subspace learning method based on semi-supervised neighborhood preserving discriminant learning, which we call Semi-supervised Neighborhood Preserving Discriminant Embedding (SNPDE). The method preserves the local neighborhood structure of face manifold using NPE, and maximizes the separability of different classes using LDA. Experimental results on two face databases demonstrate the effectiveness of the proposed method.

Keywords

Face Recognition Face Image Unlabeled Data Face Database Generalize Eigenvalue Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maryam Mehdizadeh
    • 1
  • Cara MacNish
    • 1
  • R. Nazim Khan
    • 2
  • Mohammed Bennamoun
    • 1
  1. 1.Department of Computer Science and Software EngineeringUniversity of Western AustraliaAustralia
  2. 2.Department of MathematicsUniversity of Western AustraliaAustralia

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