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Face Recognition Using Classification-Based Linear Projections

Abstract

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.

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Correspondence to Moshe Butman.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Butman, M., Goldberger, J. Face Recognition Using Classification-Based Linear Projections. EURASIP J. Adv. Signal Process. 2008, 416318 (2008). https://doi.org/10.1155/2008/416318

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Keywords

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