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Eigenspectra Versus Eigenfaces: Classification with a Kernel-Based Nonlinear Representor

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

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Abstract

This short paper proposes a face recognition scheme, wherein features called eigenspectra are extracted successively by the fast Fourier transform (FFT) and the principle component analysis (PCA) and classification results are obtained by a classifier called kernel-based nonlinear representor (KNR). Its effectiveness is shown by experimental results on the Olivetti Research Laboratory (ORL) face database.

Supported by the Key Project of Chinese Ministry of Education (No.105150). Thanks to Prof. H. Ogawa of Tokyo Institute of Technology for helpful discussions.

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References

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology, Chengdu, 610054, China

    Benyong Liu & Jing Zhang

Authors
  1. Benyong Liu
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  2. Jing Zhang
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Liu, B., Zhang, J. (2005). Eigenspectra Versus Eigenfaces: Classification with a Kernel-Based Nonlinear Representor. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_83

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  • DOI: https://doi.org/10.1007/11539087_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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