Department of Public Studies, Henan Vocational College of Nursing, Anyang 455000, China.
School of Continuing Education, China University of Mining and Technology, Xuzhou 221008, China.
Comput Intell Neurosci. 2022 Mar 2;2022:4068414. doi: 10.1155/2022/4068414. eCollection 2022.
Gesture recognition utilizes deep learning network model to automatically extract deep features of data; however, traditional machine learning algorithms rely on manual feature extraction and poor model generalization ability. In this paper, a multimodal gesture recognition algorithm based on convolutional long-term memory network is proposed. First, a convolutional neural network (CNN) is employed to automatically extract the deeply hidden features of multimodal gesture data. Then, a time series model is constructed using a long short-term memory (LSTM) network to learn the long-term dependence of multimodal gesture features on the time series. On this basis, the classification of multimodal gestures is realized by the SoftMax classifier. Finally, the method is experimented and evaluated on two dynamic gesture datasets, VIVA and NVGesture. Experimental results indicate that the accuracy rates of the proposed method on the VIVA and NVGesture datasets are 92.55% and 87.38%, respectively, and its recognition accuracy and convergence performance are better than those of other comparison algorithms.
手势识别利用深度学习网络模型自动提取数据的深层特征；然而，传统的机器学习算法依赖于手动特征提取和较差的模型泛化能力。本文提出了一种基于卷积长短期记忆网络的多模态手势识别算法。首先，采用卷积神经网络（CNN）自动提取多模态手势数据的深层隐藏特征。然后，使用长短期记忆（LSTM）网络构建时间序列模型，以学习多模态手势特征在时间序列上的长期依赖性。在此基础上，通过 SoftMax 分类器实现多模态手势的分类。最后，在两个动态手势数据集 VIVA 和 NVGesture 上进行了实验和评估。实验结果表明，所提出方法在 VIVA 和 NVGesture 数据集上的准确率分别为 92.55%和 87.38%，其识别准确率和收敛性能优于其他对比算法。
