Regularized discriminant analysis for the small sample size problem in face recognition

Authors:
Juwei Lu;K. N. Plataniotis;A. N. Venetsanopoulos
Affiliations:
Bell Canada Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ont., Canada M5S 3G4;Bell Canada Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ont., Canada M5S 3G4;Bell Canada Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ont., Canada M5S 3G4
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

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 trainings 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.