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Experiments with Rough Set Approach to Face Recognition

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Book cover Rough Sets and Current Trends in Computing (RSCTC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5306))

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Abstract

The article reports our experiences with the application of the hierarchy of probabilistic decision tables to face recognition. The methodology underlying the classifier development for our experiments is the variable precision rough sets, a probabilistic extension of the rough set theory. The soft-cut classifier method and the related theoretical background, the feature extraction technique based on the principal component analysis and the experimental results are presented.

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Author information

Authors and Affiliations

  1. Department of Computer Science University of Regina, Regina, SK, S4S 0A2, Canada

    Xuguang Chen & Wojciech Ziarko

Authors
  1. Xuguang Chen
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  2. Wojciech Ziarko
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Editor information

Editors and Affiliations

  1. Department of Computer Science, The University of Akron, OH 44325-4003, Akron, USA

    Chien-Chung Chan

  2. Department of Electrical Engineering and Computer Science, University of Kansas, KS 66045, Lawrence, USA

    Jerzy W. Grzymala-Busse

  3. Department of Computer Science, University of Regina,, S4S 0A2, Regina, Saskatchewan, Canada

    Wojciech P. Ziarko

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© 2008 Springer-Verlag Berlin Heidelberg

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Chen, X., Ziarko, W. (2008). Experiments with Rough Set Approach to Face Recognition. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_41

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  • DOI: https://doi.org/10.1007/978-3-540-88425-5_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88423-1

  • Online ISBN: 978-3-540-88425-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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