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Neural Computing and Applications

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01.12.2013 | Original Article

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

verfasst von: Minghua Wan, Wenming Zheng

Erschienen in: Neural Computing and Applications | Sonderheft 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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Titel: Fuzzy two-dimensional local graph embedding discriminant analysis (F2DLGEDA) with its application to face and palm biometrics
verfasst von: Minghua Wan
Wenming Zheng
Publikationsdatum: 01.12.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1317-3

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