Mixture-of-Laplacian Faces and Its Application to Face Recognition

  • S. Noushath
  • Ashok Rao
  • G. Hemantha Kumar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)

Abstract

The locality preserving projection (LPP), known as Laplacianfaces, was recently proposed as a transformation technique of mapping which optimally preserves the neighborhood structure of the dataset. In this paper, an efficient method for face recognition called mixture-of-Laplacianfaces (or LPP mixture model) is proposed, which obtains several sets of Laplacianfaces through Expectation-Maximization (EM) learning of Gaussian Mixture Models (GMM). Experiments carried out by using this on ORL, FERET and COIL-20 indicate superior performance as compared with method based on Laplacianfaces and other contemporary subspace methods.

Keywords

Training Sample Face Recognition Gaussian Mixture Model Training Image Recognition Accuracy 
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 2007

Authors and Affiliations

  • S. Noushath
    • 1
  • Ashok Rao
    • 2
  • G. Hemantha Kumar
    • 1
  1. 1.Dept of Studies in Computer Science,University of Mysore, Mysore - 570 006India
  2. 2.Dept of Electronics and Communication, SJ College of Engineering, Mysore - 570 006India

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