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Face Recognition Using Kernel Scatter-Difference-Based Discriminant Analysis | IEEE Journals & Magazine | IEEE Xplore

Face Recognition Using Kernel Scatter-Difference-Based Discriminant Analysis


Abstract:

There are two fundamental problems with the Fisher linear discriminant analysis for face recognition. One is the singularity problem of the within-class scatter matrix du...Show More

Abstract:

There are two fundamental problems with the Fisher linear discriminant analysis for face recognition. One is the singularity problem of the within-class scatter matrix due to small training sample size. The other is that it cannot efficiently describe complex nonlinear variations of face images because of its linear property. In this letter, a kernel scatter-difference-based discriminant analysis is proposed to overcome these two problems. We first use the nonlinear kernel trick to map the input data into an implicit feature space F. Then a scatter-difference-based discriminant rule is defined to analyze the data in F. The proposed method can not only produce nonlinear discriminant features but also avoid the singularity problem of the within-class scatter matrix. Extensive experiments show encouraging recognition performance of the new algorithm.
Published in: IEEE Transactions on Neural Networks ( Volume: 17, Issue: 4, July 2006)
Page(s): 1081 - 1085
Date of Publication: 15 February 2024

ISSN Information:

PubMed ID: 16856670

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