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Unsupervised Learning Based Static Hand Gesture Recognition from RGB-D Sensor

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Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016) (SoCPaR 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 614))

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Abstract

In this paper, we propose a novel, real-time static hand gesture recognition framework based on unsupervised learning. We use unlabelled training data from an RGB-D (RGB, Depth) sensor and use the depth data to detect the hand(s) in the image. We believe that the gesture is dependent on the shape of the hand, which is defined by number of extended/open fingers and which fingers are extended/open. We use unsupervised learning techniques to detect and label the extended fingers in the training data. We also use the same algorithm to detect and label the extended fingers in the test images on-the-fly. Our gesture recognition system is independent of the orientation of the gesturing hand and arm. Moreover, it does not require any markers and is completely unobtrusive, making it suitable for assistive living paradigms.

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Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Bindu Verma & Ayesha Choudhary

Authors
  1. Bindu Verma
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  2. Ayesha Choudhary
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Corresponding author

Correspondence to Ayesha Choudhary .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  2. VIT University, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. School of Engineering, Polytechnic of Porto (ISEP/IPP), Porto, Portugal

    Ana Maria Madureira

  4. Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Verma, B., Choudhary, A. (2018). Unsupervised Learning Based Static Hand Gesture Recognition from RGB-D Sensor. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_30

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  • DOI: https://doi.org/10.1007/978-3-319-60618-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60617-0

  • Online ISBN: 978-3-319-60618-7

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