A Discriminative Non-linear Manifold Learning Technique for Face Recognition

  • Bogdan Raducanu
  • Fadi Dornaika
Part of the Communications in Computer and Information Science book series (CCIS, volume 254)


In this paper we propose a novel non-linear discriminative analysis technique for manifold learning. The proposed approach is a discriminant version of Laplacian Eigenmaps which takes into account the class label information in order to guide the procedure of non-linear dimensionality reduction. By following the large margin concept, the graph Laplacian is split in two components: within-class graph and between-class graph to better characterize the discriminant property of the data.

Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variance in their appearance.


Face Recognition Locally Linear Embedding Average Recognition Rate Nonlinear Dimensionality Reduction Locality Preserve Projection 
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

  • Bogdan Raducanu
    • 1
  • Fadi Dornaika
    • 2
    • 3
  1. 1.Computer Vision CenterBellaterraSpain
  2. 2.IKERBASQUEBasque Foundation for ScienceSpain
  3. 3.University of the Basque CountrySan SebastianSpain

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