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.
@article{arxiv.1905.07917,
title = {Skeleton-Based Hand Gesture Recognition by Learning SPD Matrices with Neural Networks},
author = {Xuan Nguyen and Luc Brun and Olivier Lezoray and Sébastien Bougleux},
journal= {arXiv preprint arXiv:1905.07917},
year = {2019}
}