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.
Abstract (translated)
本文提出了一种利用神经网络学习SPD矩阵的基于骨骼数据的手势识别方法。以手关节三维坐标为输入,将手骨架建模为图形,引入一种用于SPD矩阵学习的神经网络。该网络基于两个新设计的层,将一组SPD矩阵转换为SPD矩阵。对于手势识别,我们使用从我们的网络中提取的特征训练一个线性支持向量机分类器。一个具有挑战性的数据集(来自SHREC 2017 3D形状检索竞赛的动态手势数据集)的实验结果表明,该方法优于最先进的方法。
URL
https://arxiv.org/abs/1905.07917