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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2013

01-12-2013 | Original Article

Fuzzy two-dimensional local graph embedding discriminant analysis (F2DLGEDA) with its application to face and palm biometrics

Authors: Minghua Wan, Wenming Zheng

Published in: Neural Computing and Applications | Special Issue 1/2013

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Abstract

In two-dimensional local graph embedding discriminant analysis, the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring within the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. But in the real world, face images are always affected by variations in illumination conditions and different facial expressions. So, the fuzzy two-dimensional local graph embedding analysis algorithm is proposed, in which the fuzzy k-nearest neighbor is implemented to achieve the distribution local information of original samples. Experimental results on the ORL, Yale face and on the PolyU palmprint databases show the effectiveness of the proposed method.
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Metadata
Title
Fuzzy two-dimensional local graph embedding discriminant analysis (F2DLGEDA) with its application to face and palm biometrics
Authors
Minghua Wan
Wenming Zheng
Publication date
01-12-2013
Publisher
Springer London
Published in
Neural Computing and Applications / Issue Special Issue 1/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1317-3

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