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Skeleton-Based Hand Gesture Recognition by Learning SPD Matrices with Neural Networks

Xuan Son Nguyen 1 Luc Brun 1 Olivier Lezoray 1 Sébastien Bougleux 1 
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : In this paper, we propose a new hand gesture recognition method based on skeletal data by learning SPD matrices with neural networks. We model the hand skeleton as a graph and introduce a neural network for SPD matrix learning, taking as input the 3D coordinates of hand joints. The proposed network is based on two newly designed layers that transform a set of SPD matrices into a SPD matrix. For gesture recognition, we train a linear SVM classifier using features extracted from our network. Experimental results on a challenging dataset (Dynamic Hand Gesture dataset from the SHREC 2017 3D Shape Retrieval Contest) show that the proposed method outperforms state-of-the-art methods.
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Contributor : Luc Brun Connect in order to contact the contributor
Submitted on : Sunday, May 19, 2019 - 4:12:17 PM
Last modification on : Saturday, June 25, 2022 - 9:53:28 AM


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  • HAL Id : hal-02133684, version 1
  • ARXIV : 1905.07917


Xuan Son Nguyen, Luc Brun, Olivier Lezoray, Sébastien Bougleux. Skeleton-Based Hand Gesture Recognition by Learning SPD Matrices with Neural Networks. 14th IEEE International Conference on Automatic Face and Gesture Recognition, May 2019, Lille, France. ⟨hal-02133684⟩



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