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Global–local fisher discriminant approach for face recognition

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Abstract

In this paper, we proposed a linear discriminant approach, namely global–local Fisher discriminant analysis (GLFDA) that explicitly considers both the local and global discriminant structures embedded in data. To be specific, GLFDA constructs two graphs to, respectively, model the global and local discriminant structures and then incorporates discriminant structures and local intrinsic structure, which characterizes the within-class compactness, into the objective function for dimensionality reduction. Thus, GLFDA well encodes the discriminant information, especially the local discriminant information of data. Experimental results on AR, YALE, and UMIST databases show the effectiveness of the proposed algorithm.

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Acknowledgments

We would like to thank the anonymous reviewers and AE for their constructive comments and suggestions. This work is supported by the National Natural Science Foundation of China under Grant 61271296, the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2012JM8002, China Postdoctoral Science Foundation under Grant 2012M521747, and the 111 Project of China (B08038).

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Authors and Affiliations

  1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, 710071, China

    Qianqian Wang, Xiaolei Hu, Quanxue Gao & Bing Li

  2. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Yong Wang

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  1. Qianqian Wang
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  2. Xiaolei Hu
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  3. Quanxue Gao
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  4. Bing Li
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  5. Yong Wang
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Correspondence to Quanxue Gao.

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Wang, Q., Hu, X., Gao, Q. et al. Global–local fisher discriminant approach for face recognition. Neural Comput & Applic 25, 1137–1144 (2014). https://doi.org/10.1007/s00521-014-1592-2

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  • DOI: https://doi.org/10.1007/s00521-014-1592-2

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