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A New Gait-Based Identification Method Using Local Gauss Maps

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9008))

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Abstract

We propose a new descriptor for human identification based on gait. The current and most prevailing trend in gait representation revolves around encoding body shapes as silhouettes averaged over gait cycles. Our method, however, captures geometric properties of the silhouettes boundaries. Namely, we evaluate contour curvatures locally using Gauss maps. This results in an improved shape representation, as contrasted to average silhouettes. In addition, our approach does not require prior training. We thoroughly demonstrate the superiority of our method in gait-based human identification compared to state-of-the-art approaches. We use the OU-ISIR Large Population dataset, with over 4000 subjects captured at different viewing angles, to provide statistically reliable results.

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Acknowledgement

This work was supported in part by the JST CREST “Behavior Understanding based on Intention-Gait Model” project.

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Authors and Affiliations

  1. The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan

    Hazem El-Alfy, Ikuhisa Mitsugami & Yasushi Yagi

  2. Alexandria University, Alexandria, Egypt

    Hazem El-Alfy

Authors
  1. Hazem El-Alfy
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  2. Ikuhisa Mitsugami
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  3. Yasushi Yagi
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Correspondence to Hazem El-Alfy .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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El-Alfy, H., Mitsugami, I., Yagi, Y. (2015). A New Gait-Based Identification Method Using Local Gauss Maps. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_1

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  • Publisher Name: Springer, Cham

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