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Advances in Mathematical Physics
Volume 2013 (2013), Article ID 825861, 9 pages
http://dx.doi.org/10.1155/2013/825861
Research Article

Geometric Distribution Weight Information Modeled Using Radial Basis Function with Fractional Order for Linear Discriminant Analysis Method

1College of Mathematics and Computational Science, Shenzhen University, Shenzhen 518160, China
2College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518160, China
3College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Received 20 September 2013; Accepted 24 September 2013

Academic Editor: Ming Li

Copyright © 2013 Wen-Sheng Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Fisher linear discriminant analysis (FLDA) is a classic linear feature extraction and dimensionality reduction approach for face recognition. It is known that geometric distribution weight information of image data plays an important role in machine learning approaches. However, FLDA does not employ the geometric distribution weight information of facial images in the training stage. Hence, its recognition accuracy will be affected. In order to enhance the classification power of FLDA method, this paper utilizes radial basis function (RBF) with fractional order to model the geometric distribution weight information of the training samples and proposes a novel geometric distribution weight information based Fisher discriminant criterion. Subsequently, a geometric distribution weight information based LDA (GLDA) algorithm is developed and successfully applied to face recognition. Two publicly available face databases, namely, ORL and FERET databases, are selected for evaluation. Compared with some LDA-based algorithms, experimental results exhibit that our GLDA approach gives superior performance.