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

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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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Acknowledgments

This work is partially supported by the National Science Foundation of China under grant no. 61203243, 61272077, 61202319, 61201439, 60963002, the National Science Foundation of Jiangxi Provincial under grant no. 20114BAB201034, 20122BAB211025, China’s Aviation Science no. 20115556007 and Youth Foundation of Jiangxi Provincial Department of Education no. GJJ12459.

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Authors and Affiliations

  1. School of Information Science and Engineering, Southeast University, Nanjing, 210096, China

    Minghua Wan & Wenming Zheng

  2. School of Information Engineering, Nanchang Hangkong University, Nanchang, 330063, China

    Minghua Wan

Authors
  1. Minghua Wan
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  2. Wenming Zheng
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Correspondence to Minghua Wan.

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Wan, M., Zheng, W. Fuzzy two-dimensional local graph embedding discriminant analysis (F2DLGEDA) with its application to face and palm biometrics. Neural Comput & Applic 23 (Suppl 1), 201–207 (2013). https://doi.org/10.1007/s00521-012-1317-3

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  • DOI: https://doi.org/10.1007/s00521-012-1317-3

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