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Features Selection for the Most Accurate SVM Gender Classifier Based on Geometrical Features

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10842))

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Abstract

In the paper, we have focused on the problem of choosing the best set of features in the task of gender classification/recognition. Choosing a minimum set of features, that can give satisfactory results is also important in the case where only a part of the face is visible. The minimum set of features can simplify the classification process to make it useful for mobile applications. Many authors have used SVM in facial classification and recognition problems, but there are not many works using facial geometry features in the classification neither in SVM. Almost all works are based on the appearance-based methods. In the paper, we show that the classifier constructed on the base of only two or three geometric facial features can give satisfactory (though not always optimal) results with accuracy 82% and positive predictive value 87%, also in incomplete facial images. We show that Matlab and Mathematica can produce very different SVMs given the same data.

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Author information

Authors and Affiliations

  1. Faculty of Physics and Applied Informatics, University of Lodz, Pomorska Street 149/153, 90-236, Lodz, Poland

    Piotr Milczarski & Zofia Stawska

  2. Department of Visual and Human Centred Computing, Dundalk Institute of Technology, Dundalk, Country Louth, Ireland

    Shane Dowdall

Authors
  1. Piotr Milczarski
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  2. Zofia Stawska
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  3. Shane Dowdall
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Corresponding author

Correspondence to Piotr Milczarski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Milczarski, P., Stawska, Z., Dowdall, S. (2018). Features Selection for the Most Accurate SVM Gender Classifier Based on Geometrical Features. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-91262-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91261-5

  • Online ISBN: 978-3-319-91262-2

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