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Gait Based Gender Recognition Using Sparse Spatio Temporal Features

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MultiMedia Modeling (MMM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8326))

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Abstract

A gender balanced dataset of 101 pedestrians on a treadmill is presented. Gait is analysed for gender classification using a modification of a framework which has previously proven effective when used in behaviour recognition experiments. Sparse spatio temporal features from the video clips are classified using Support Vector Machines. Tuning parameters are investigated to find an effective feature descriptor for gender separation and an accuracy of 87% is achieved.

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Author information

Authors and Affiliations

  1. Centre for Secure Information Technologies, Queen’s University Belfast, Northern Ireland Science Park, Queen’s Road, Queen’s Island, Belfast, BT3 9DT, UK

    Matthew Collins, Paul Miller & Jianguo Zhang

Authors
  1. Matthew Collins
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  2. Paul Miller
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  3. Jianguo Zhang
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Editor information

Editors and Affiliations

  1. School of Computing, Dublin City University, 9, Dublin, Ireland

    Cathal Gurrin

  2. Fakultät IV für Elektrotechnik und Informatik, Technische Universität Berlin / DAI-Labor, 10587, Berlin, Germany

    Frank Hopfgartner

  3. Department of Information and Computing Sciences, Universiteit Utrecht, 3584, Utrecht, CC, The Netherlands

    Wolfgang Hurst

  4. UiT The Arctic University of Norway, 9019, Tromsø, Norway

    Håvard Johansen

  5. Singapore University of Technology and Design, Singapore

    Hyowon Lee

  6. School of Electrical Engineering, Dublin City University, Ireland

    Noel O’Connor

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© 2014 Springer International Publishing Switzerland

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Collins, M., Miller, P., Zhang, J. (2014). Gait Based Gender Recognition Using Sparse Spatio Temporal Features. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8326. Springer, Cham. https://doi.org/10.1007/978-3-319-04117-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-04117-9_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04116-2

  • Online ISBN: 978-3-319-04117-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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