Due to the recent development of machine learning and sensor innovations, hand gesture recognition systems become promising for the digital entertainment field. In this paper, we propose a dynamic hand gesture recognition approach using touchless hand motions over a Leap Motion device. First, we analyze the sequential time series data gathered from Leap Motion using Long Short-Term Memory (LSTM) recurrent neural networks for recognition purposes. We exploit basic unidirectional LSTM and bidirectional LSTM separately. Then, we propound novel architecture by combining the aforementioned models with additional components to give a final prediction network, named Hybrid Bidirectional Unidirectional LSTM (HBU-LSTM). The suggested network improves the model performance significantly by considering the spatial and temporal dependencies between the Leap Motion data and the network layers during the forward and backward pass. The recognition models are examined on two available benchmark datasets, named the LeapGestureDB dataset and the RIT dataset. Experiments demonstrate the potential of the proposed HBU-LSTM network for dynamic hand gesture recognition, with an average recognition rate reaching approximately 90%. Our suggested approach reaches superior performance, in terms of accuracy and computational complexity, over some existing methods for hand gesture recognition.

由于机器学习和传感器创新的最新发展，手势识别系统在数字娱乐领域变得很有前途。在本文中，我们提出了一种在Leap Motion设备上使用非接触式手势的动态手势识别方法。首先，我们使用长短期记忆（LSTM）递归神经网络分析从Leap Motion收集的顺序时间序列数据，以进行识别。我们分别利用基本的单向LSTM和双向LSTM。然后，我们通过将上述模型与其他组件组合以提供最终的预测网络，即混合双向单向LSTM（HBU-LSTM），来提出新颖的体系结构。建议的网络通过在向前和向后传递过程中考虑Leap Motion数据与网络层之间的时空相关性，从而显着改善模型性能。在两个可用的基准数据集（称为LeapGestureDB数据集和RIT数据集）上检查了识别模型。实验证明了所提出的HBU-LSTM网络在动态手势识别方面的潜力，平均识别率约为90％。我们提出的方法在准确性和计算复杂性方面要优于一些现有的手势识别方法。命名为LeapGestureDB数据集和RIT数据集。实验证明了所提出的HBU-LSTM网络在动态手势识别方面的潜力，平均识别率约为90％。我们提出的方法在准确性和计算复杂性方面要优于现有的一些手势识别方法。命名为LeapGestureDB数据集和RIT数据集。实验证明了所提出的HBU-LSTM网络在动态手势识别方面的潜力，平均识别率约为90％。我们提出的方法在准确性和计算复杂性方面要优于现有的一些手势识别方法。