Inverse Fisher discriminate criteria for small sample size problem and its application to face recognition

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2192-2194
Journal / PublicationPattern Recognition
Volume38
Issue number11
Publication statusPublished - Nov 2005
Externally publishedYes

Abstract

This paper addresses the small sample size problem in linear discriminant analysis, which occurs in face recognition applications. Belhumeur et al. [IEEE Trans. Pattern Anal. Mach. Intell. 19 (7) (1997) 711-720] proposed the FisherFace method. We find out that the FisherFace method might fail since after the PCA transform the corresponding within class covariance matrix can still be singular, this phenomenon is verified with the Yale face database. Hence we propose to use an inverse Fisher criteria. Our method works when the number of training images per class is one. Experiment results suggest that this new approach performs well. © 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Research Area(s)

  • Face recognition, Inverse Fisher discriminate criteria, Linear discriminant analysis, Small sample size problem