Hand gesture recognition methods play an important role in human-computer interaction. Among these methods are skeleton-based recognition techniques that seem to be promising. In literature, several methods have been proposed to recognize hand gestures with skeletons. One problem with these methods is that they consider little the connectivity between the joints of a skeleton, constructing simple graphs for skeleton connectivity. Observing this, we built a new model of hand skeletons by adding three types of edges in the graph to finely describe the linkage action of joints. Then, an end-to-end deep neural network, hand gesture graph convolutional network, is presented in which the convolution is conducted only on linked skeleton joints. Since the training dataset is relatively small, this work proposes expanding the coordinate dimensionality so as to let models learn more semantic features. Furthermore, relative coordinates are employed to help hand gesture graph convolutional network learn the feature representation independent of the random starting positions of actions. The proposed method is validated on two challenging datasets, and the experimental results show that it outperforms the state-of-the-art methods. Furthermore, it is relatively lightweight in practice for hand skeleton-based gesture recognition.

中文翻译：

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基于骨架的动态手势识别的时空图卷积网络

手势识别方法在人机交互中起着重要作用。在这些方法中，基于骨架的识别技术似乎很有前途。在文献中，已经提出了几种方法来识别具有骨骼的手势。这些方法的一个问题是，它们很少考虑骨架关节之间的连通性，而是为骨架连通性构建简单的图形。观察到这一点，我们通过在图形中添加三种类型的边缘以精细描述关节的链接动作，建立了新的手部骨骼模型。然后，提出了一种端到端的深度神经网络，即手势图卷积网络，其中仅在链接的骨骼关节上进行卷积。由于训练数据集相对较小，这项工作提出扩大坐标的维数，以使模型学习更多的语义特征。此外，采用相对坐标来帮助手势图卷积网络学习独立于动作的随机起始位置的特征表示。该方法在两个具有挑战性的数据集上得到了验证，实验结果表明，该方法优于最新方法。此外，对于基于手骨架的手势识别，它在实践中相对较轻。实验结果表明它优于最新方法。此外，对于基于手骨架的手势识别，它在实践中相对较轻。实验结果表明它优于最新方法。此外，对于基于手骨架的手势识别，它在实践中相对较轻。