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Neural Computing and Applications

Erschienen in:

01.09.2009 | Original Article

Kernel optimization-based discriminant analysis for face recognition

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

Erschienen in: Neural Computing and Applications | Ausgabe 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.

Vorheriger Artikel Local excitation solutions in one-dimensional neural fields by external input stimuli

Nächster Artikel Face recognition using Gabor-based complete Kernel Fisher Discriminant analysis with fractional power polynomial models

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Titel: Kernel optimization-based discriminant analysis for face recognition
verfasst von: Jun-Bao Li
Jeng-Shyang Pan
Zhe-Ming Lu
Publikationsdatum: 01.09.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 6/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0282-y

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