Hand-aware graph convolution network for skeleton-based sign language recognition

Juan Song , Huixuechun Wang , Jianan Li , Jian Zheng , Zhifu Zhao , Qingshan Li
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Abstract

Skeleton-based sign language recognition (SLR) is a challenging research area mainly due to the fast and complex hand movement. Currently, graph convolution networks (GCNs) have been employed in skeleton-based SLR and achieved remarkable performance. However, existing GCN-based SLR methods suffer from a lack of explicit attention to hand topology which plays an important role in the sign language representation. To address this issue, we propose a novel hand-aware graph convolution network (HA-GCN) to focus on hand topological relationships of skeleton graph. Specifically, a hand-aware graph convolution layer is designed to capture both global body and local hand information, in which two sub-graphs are defined and incorporated to represent hand topology information. In addition, in order to eliminate the over-fitting problem, an adaptive DropGraph is designed in construction of hand-aware graph convolution block to remove the spatial and temporal redundancy in the sign language representation. With the aim to further improve the performance, the joints information, bones, together with their motion information are simultaneously modeled in a multi-stream framework. Extensive experiments on the two open-source datasets, AUTSL and INCLUDE, demonstrate that our proposed algorithm outperforms the state-of-the-art with a significant margin. Our code is available at https://github.com/snorlaxse/HA-SLR-GCN.
基于骨架的手语识别的手感图卷积网络
基于骨骼的手语识别是一个具有挑战性的研究领域,主要是由于手部运动的快速和复杂。目前,图卷积网络(GCNs)已被应用于基于骨架的单反中,并取得了显著的性能。然而,现有的基于gcn的单反方法缺乏对手部拓扑的明确关注,而手部拓扑在手语表征中起着重要作用。为了解决这一问题,我们提出了一种新的手感知图卷积网络(HA-GCN)来关注骨架图的手拓扑关系。具体而言,设计了一个手感知图卷积层来捕获全局和局部的手信息,其中定义并合并了两个子图来表示手的拓扑信息。此外,为了消除过拟合问题,在构建手感图卷积块时设计了自适应DropGraph,以消除手语表示中的时空冗余。为了进一步提高性能,关节信息、骨骼及其运动信息在多流框架中同时建模。在两个开源数据集(AUTSL和INCLUDE)上进行的大量实验表明,我们提出的算法在很大程度上优于最先进的算法。我们的代码可在https://github.com/snorlaxse/HA-SLR-GCN上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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