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What is Skeleton-Based Sign Language Recognition with Attention-Enhanced Graph Convolutional Networks about?

The natural language processing of sign language is an important task in the field of artificial intelligence and information processing. In this paper, we propose an attention-enhanced graph convolutional networks (AEGCNs) for sign language recognition (SLR). First, there are four kinds of adaptive graphs for graph convolution and each graph topology can be either uniformly or individually learned based on the skeleton data in an end-to-end manner. In addition, we employ the spatial-temporal-channel attention mechanisms to give higher weight to the relative important joints, frames and features, and the higher-order connection with Chebychev polynomial approximation to enlarge the receptive field of graph convolution. Meanwhile, the information of both the joints and bones is simultaneously modeled in a framework, which further improves the representation of the movement about hand and finger. Finally, experiments on the DEVISIGN-D, DSL50 and ASL20 datasets show that the accuracies for top1 of three datasets reach 82.96%, 95.09% and 90.23% respectively and the accuracies for top5 of three datasets achieve 96.07%, 99.18% and 100% respectively. Compared with ST-GCN and BHOF, the acc

Author

Wuyan Liang; Xiaolong Xu

Publisher

Springer International Publishing : Imprint: Springer

Published

2021

Language

EN

ISBN

9783030884796

Subjects

Computer Science, Science, Mathematics