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Neural Computing and Applications
Published in: Neural Computing and Applications 6/2009

01-09-2009 | Original Article

Kernel optimization-based discriminant analysis for face recognition

Authors: Jun-Bao Li, Jeng-Shyang Pan, Zhe-Ming Lu

Published in: Neural Computing and Applications | Issue 6/2009

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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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Metadata
Title
Kernel optimization-based discriminant analysis for face recognition
Authors
Jun-Bao Li
Jeng-Shyang Pan
Zhe-Ming Lu
Publication date
01-09-2009
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 6/2009
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-009-0282-y

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