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Self-adaptive Biometric Classifier Working on the Reduced Dataset

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Hybrid Artificial Intelligence Systems (HAIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8480))

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Abstract

The paper presents a method of object recognition by means of a reduced data set. These data are specially prepared. The proposed method was also compared with two other well-known data reduction techniques, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). Objects can mostly be described through many features but these features can have different discriminant powers. The Hotelling’s statistical method, allows determining the best discriminatory features and similarity measures which can be simultaneously selected.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, 41-200 Sosnowiec, ul Bedzinska 39, Katowice, Poland

    Piotr Porwik & Rafal Doroz

Authors
  1. Piotr Porwik
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  2. Rafal Doroz
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios Polycarpou

  2. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, SP, Brazil

    André C. P. L. F. de Carvalho

  3. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of Salamanca, Plaza de la Merced S/N, 37008 Salamanca, Spain and University of A Coruna, Escuela Universitaria Politecnica, Departamento de Enxeñeria Industrial, A Coruna, Spain

    Héctor Quintian

  6. University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

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© 2014 Springer International Publishing Switzerland

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Porwik, P., Doroz, R. (2014). Self-adaptive Biometric Classifier Working on the Reduced Dataset. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_34

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  • DOI: https://doi.org/10.1007/978-3-319-07617-1_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07616-4

  • Online ISBN: 978-3-319-07617-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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