Hand Gesture Recognition for Sign Language Using 3DCNN
Hand Gesture Recognition for Sign Language Using 3DCNN
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DOI：
10.1109/access.2020.2990434
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Mekhtiche, Mohamed Amine
中科院分区：
文献类型：
--
作者：
Al-Hammadi, Muneer;Muhammad, Ghulam;Mekhtiche, Mohamed Amine
最近，自动手势识别由于两个主要原因而变得越来越重要：失聪和听力障碍人口的增长，以及基于视觉的应用程序和无处不在的设备上的非接触式控制的发展。由于手势识别是手语分析的核心，因此强大的手势识别系统应考虑空间和时间特征。不幸的是，为手势序列找到有区别的时空描述符并不是一项简单的任务。在这项研究中，我们提出了一种有效的深度卷积神经网络方法来进行手势识别。所提出的方法采用迁移学习来克服大型标记手势数据集的稀缺性。我们使用彩色视频中的三个手势数据集对其进行了评估：这些数据集中使用了 40、23 和 10 个类别。该方法在三个数据集上针对手语依赖模式分别获得了 98.12%、100% 和 76.67% 的识别率。对于独立于签名者的模式，它在三个数据集上分别获得了 84.38%、34.9% 和 70% 的识别率。
Recently, automatic hand gesture recognition has gained increasing importance for two principal reasons: the growth of the deaf and hearing-impaired population, and the development of vision-based applications and touchless control on ubiquitous devices. As hand gesture recognition is at the core of sign language analysis a robust hand gesture recognition system should consider both spatial and temporal features. Unfortunately, finding discriminative spatiotemporal descriptors for a hand gesture sequence is not a trivial task. In this study, we proposed an efficient deep convolutional neural networks approach for hand gesture recognition. The proposed approach employed transfer learning to beat the scarcity of a large labeled hand gesture dataset. We evaluated it using three gesture datasets from color videos: 40, 23, and 10 classes were used from these datasets. The approach obtained recognition rates of 98.12%, 100%, and 76.67% on the three datasets, respectively for the signer-dependent mode. For the signer-independent mode, it obtained recognition rates of 84.38%, 34.9%, and 70% on the three datasets, respectively.