Direct Neighborhood Discriminant Analysis for Face Recognition

Miao Cheng, Bin Fang, Yuan Yan Tang and Jing Wen

Mathematical Problems in Engineering, 2008, vol. 2008, 1-15

Abstract:

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.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:825215

DOI: 10.1155/2008/825215

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