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3D Hand Gesture Recognition by Analysing Set-of-Joints Trajectories

  • Quentin De Smedt
  • Hazem Wannous
  • Jean-Philippe Vandeborre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10188)

Abstract

Hand gesture recognition is recently becoming one of the most attractive field of research in Pattern Recognition. In this paper, a skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we consider the sequential data of hand geometric configuration to capture the hand shape variation, and explore the temporal character of hand motion. 3D Hand gesture are represented as a set of relevant spatiotemporal motion trajectories of hand-parts in an Euclidean space. Trajectories are then interpreted as elements lying on Riemannian manifold of shape space to capture their shape variations and achieve gesture recognition using a linear SVM classifier.

The proposed approach is evaluated on a challenging hand gesture dataset containing 14 gestures, performed by 20 participants performing the same gesture with two different numbers of fingers. Experimental results show that our skeleton-based approach consistently achieves superior performance over a depth-based approach.

Keywords

Gesture recognition Riemaniann manifold Hand skeleton Depth image 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Quentin De Smedt
    • 1
  • Hazem Wannous
    • 2
  • Jean-Philippe Vandeborre
    • 1
  1. 1.Télécom Lille, CNRS, UMR 9189 - CRIStALLilleFrance
  2. 2.Télécom Lille, Univ. Lille, CNRS, UMR 9189 - CRIStALLilleFrance

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