  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.
