An Improved Linear Discriminant Analysis Method and its Application to Face Recognition

Kai Li; Peng Tang

doi:10.4028/www.scientific.net/AMM.556-562.4825

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562An Improved Linear Discriminant Analysis Method...

An Improved Linear Discriminant Analysis Method and its Application to Face Recognition

Abstract:

Linear discriminant analysis (LDA) is an important feature extraction method. This paper proposes an improved linear discriminant analysis method, which redefines the within-class scatter matrix and introduces the normalized parameter to control the bias and variance of eigenvalues. In addition, it makes the between-class scatter matrix to weight and avoids the overlapping of neighboring class samples. Some experiments for the improved algorithm presented by us are performed on the ORL, FERET and YALE face databases, and it is compared with other commonly used methods. Experimental results show that the proposed algorithm is the effective.

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Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

4825-4829

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.4825

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Online since:

May 2014

Authors:

Kai Li*, Peng Tang

Keywords:

Between-Class Scatter, Linear Discriminant Analysis (LDA), Normalized Parameter, Within-Class Scatter

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* - Corresponding Author

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