Direct neighborhood discriminant analysis for face recognition
Fang B.; Cheng M.; Tang Y.Y.; Wen J.
Source PublicationMathematical Problems in Engineering
ISSN1024123X 15635147
AbstractFace 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.
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
AffiliationChongqing University
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GB/T 7714
Fang B.,Cheng M.,Tang Y.Y.,et al. Direct neighborhood discriminant analysis for face recognition[J]. Mathematical Problems in Engineering,2008,2008.
APA Fang B.,Cheng M.,Tang Y.Y.,&Wen J..(2008).Direct neighborhood discriminant analysis for face recognition.Mathematical Problems in Engineering,2008.
MLA Fang B.,et al."Direct neighborhood discriminant analysis for face recognition".Mathematical Problems in Engineering 2008(2008).
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