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Face Recognition Using Sparse Representations and Manifold Learning

  • Grigorios Tsagkatakis
  • Andreas Savakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)

Abstract

Manifold learning is a novel approach in non-linear dimensionality reduction that has shown great potential in numerous applications and has gained ground compared to linear techniques. In addition, sparse representations have been recently applied on computer vision problems with success, demonstrating promising results with respect to robustness in challenging scenarios. A key concept shared by both approaches is the notion of sparsity. In this paper we investigate how the framework of sparse representations can be applied in various stages of manifold learning. We explore the use of sparse representations in two major components of manifold learning: construction of the weight matrix and classification of test data. In addition, we investigate the benefits that are offered by introducing a weighting scheme on the sparse representations framework via the weighted LASSO algorithm. The underlying manifold learning approach is based on the recently proposed spectral regression framework that offers significant benefits compared to previously proposed manifold learning techniques. We present experimental results on these techniques in three challenging face recognition datasets.

Keywords

Face recognition manifold learning sparse representations 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Grigorios Tsagkatakis
    • 1
  • Andreas Savakis
    • 2
  1. 1.Center for Imaging ScienceRochester Institute of TechnologyUSA
  2. 2.Department of Computer EngineeringRochester Institute of TechnologyUSA

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