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Mathematical Problems in Engineering
Volume 2008 (2008), Article ID 825215, 15 pages
Research Article

Direct Neighborhood Discriminant Analysis for Face Recognition

Department of Computer Science, Chongqing University, Chongqing 400030, China

Received 21 March 2008; Accepted 16 April 2008

Academic Editor: Ming Li

Copyright © 2008 Miao Cheng 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.


Face recognition is a challenging problem in computer vision and pattern recognition. Recently, many local geometrical structure-based techiniques are presented to obtain the low-dimensional representation of face images with enhanced discriminatory power. However, these methods suffer from the small simple size (SSS) problem or the high computation complexity of high-dimensional data. To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA), which separates the nearby samples of interclass and preserves the local within-class geometry in two steps, respectively. In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information. Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method.