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Kernel optimization-based discriminant analysis for face recognition

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Abstract

The selection of kernel function and its parameter influences the performance of kernel learning machine. The difference geometry structure of the empirical feature space is achieved under the different kernel and its parameters. The traditional changing only the kernel parameters method will not change the data distribution in the empirical feature space, which is not feasible to improve the performance of kernel learning. This paper applies kernel optimization to enhance the performance of kernel discriminant analysis and proposes a so-called Kernel Optimization-based Discriminant Analysis (KODA) for face recognition. The procedure of KODA consisted of two steps: optimizing kernel and projecting. KODA automatically adjusts the parameters of kernel according to the input samples and performance on feature extraction is improved for face recognition. Simulations on Yale and ORL face databases are demonstrated the feasibility of enhancing KDA with kernel optimization.

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Authors and Affiliations

  1. Department of Automatic Test and Control, Harbin Institute of Technology, P.O. Box 339, Harbin, 150001, People’s Republic of China

    Jun-Bao Li

  2. Department of Electronic Engineering National Kaohsiung, University of Applied Sciences, D415 Chien-Kung Road, Kaohsiung 807, Taiwan

    Jeng-Shyang Pan

  3. Visual Information Analysis and Processing Research Center, Harbin Institute of Technology Shenzhen Graduate School, Room 202L, Building No. 4, HIT Campus Shenzhen University Town, Xili, Shenzhen, 518055, People’s Republic of China

    Zhe-Ming Lu

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  1. Jun-Bao Li
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Correspondence to Jun-Bao Li.

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Li, JB., Pan, JS. & Lu, ZM. Kernel optimization-based discriminant analysis for face recognition. Neural Comput & Applic 18, 603–612 (2009). https://doi.org/10.1007/s00521-009-0282-y

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