PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Face recognition using classification based linear projections
Moshe Butman and Jacob Goldberger
EURASIP Journal on Advances in Signal Processing Volume 8, Number 2, 2008. ISSN 1110-8657


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 was 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 data-bases.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:4484
Deposited By:Jacob Goldberger
Deposited On:13 March 2009