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Regularized discriminant analysis for the small sample size problem in face recognition
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DOI：10.1016/S0167-8655(03)00167-3.png)
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Abstract
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It is well-known that the applicability of both linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) to high-dimensional pattern classification tasks such as face recognition (FR) often suffers from the so-called small sample size (SSS) problem arising from the small number of available training samples compared to the dimensionality of the sample space. In this paper, we propose a new QDA like method that effectively addresses the SSS problem using a regularization technique. Extensive experimentation performed on the FERET database indicates that the proposed methodology outperforms traditional methods such as Eigenfaces, direct QDA and direct LDA in a number of SSS setting scenarios. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
linear discriminant analysis
quadratic discriminant analysis
small sample size
regularization
face recognition
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