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  • Research Article
  • Open Access

Face Recognition Using Classification-Based Linear Projections

EURASIP Journal on Advances in Signal Processing20082008:416318

  • Received: 24 July 2007
  • Accepted: 19 February 2008
  • Published:


Subspace methods have been successfully applied to face recognition tasks. In this study we propose a face recognition algorithm based on a linear subspace projection. The subspace is found via utilizing a variant of the neighbourhood component analysis (NCA) algorithm which is a supervised dimensionality reduction method that has been recently introduced. Unlike previously suggested supervised subspace methods, the algorithm explicitly utilizes the classification performance criterion to obtain the optimal linear projection. In addition to its feature extraction capabilities, the algorithm also finds the optimal distance-metric that should be used for face-image retrieval in the transformed space. The proposed face-recognition technique significantly outperforms traditional subspace-based approaches particulary in very low-dimensional representations. The method performance is demonstrated across a range of standard face databases.


  • Face Recognition
  • Face Database
  • Subspace Method
  • Linear Projection
  • Dimensionality Reduction Method

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Authors’ Affiliations

Computer Science Department, Bar-Ilan University, Ramat-Gan, 52900, Israel
School of Engineering, Bar-Ilan University, Ramat-Gan, 52900, Israel


© M. Butman and J. Goldberger. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.