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Computer Vision and Machine Learning with RGB-D Sensors pp 215–237Cite as

Feature Descriptors for Depth-Based Hand Gesture Recognition

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Depth data acquired by consumer depth cameras provide a very informative description of the hand pose that can be exploited for accurate gesture recognition. A typical hand gesture recognition pipeline requires to identify the hand, extract some relevant features and exploit a suitable machine learning technique to recognize the performed gesture. This chapter deals with the recognition of static poses. It starts by describing how the hand can be extracted from the scene exploiting depth and color data. Then several different features that can be extracted from the depth data are presented. Finally, a multi-class support vector machines (SVM) classifier is applied to the presented features in order to evaluate the performance of the various descriptors.

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Notes

  1. 1.

    In Eqs. (11.3) and (11.4) \(L\) is considered as a periodic function with period \(2\pi \).

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Author information

Authors and Affiliations

  1. Department of Information Engineering, Via Gradenigo 6/B, 35131, Padova, Italy

    Fabio Dominio, Giulio Marin, Mauro Piazza & Pietro Zanuttigh

Authors
  1. Fabio Dominio
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  2. Giulio Marin
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  3. Mauro Piazza
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  4. Pietro Zanuttigh
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Corresponding author

Correspondence to Fabio Dominio .

Editor information

Editors and Affiliations

  1. University of Sheffield, United Kingdom

    Ling Shao

  2. Civolution Technology, Eindhoven, The Netherlands

    Jungong Han

  3. Microsoft Research, Cambridge, United Kingdom

    Pushmeet Kohli

  4. Microsoft Research, Redmond, Washington, USA

    Zhengyou Zhang

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© 2014 Springer International Publishing Switzerland

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Dominio, F., Marin, G., Piazza, M., Zanuttigh, P. (2014). Feature Descriptors for Depth-Based Hand Gesture Recognition. In: Shao, L., Han, J., Kohli, P., Zhang, Z. (eds) Computer Vision and Machine Learning with RGB-D Sensors. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-08651-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-08651-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08650-7

  • Online ISBN: 978-3-319-08651-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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