Finger microgestures have been widely used in human computer interaction (HCI), particularly for interactive applications, such as virtual reality (VR) and augmented reality (AR) technologies, to provide immersive experience. However, traditional 2D image-based microgesture recognition suffers from low accuracy due to the limitations of 2D imaging sensors, which have no depth information. In this article, we proposed an innovative 3D microgesture recognition system based on a holoscopic 3D imaging sensor. Due to the lack of holoscopic 3D datasets, a comprehensive holoscopic 3D microgesture (HoMG) database is created and used to develop a robust 3D microgesture recognition method. Then, a fast algorithm is proposed to extract multiviewpoint images from one holoscopic image. Furthermore, we applied a CNN model with an attention-based residual block to each viewpoint image to improve the algorithm performance. Finally, bagging classification tree decision-level fusion is applied to combine the predictions. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods and delivers a better accuracy than existing methods.

中文翻译：

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基于视点图像和决策融合的深度神经网络全息3D微手势识别

手指手势已被广泛用于人机交互（HCI）中，尤其是用于交互式应用程序，例如虚拟现实（VR）和增强现实（AR）技术，以提供沉浸式体验。然而，由于2D成像传感器的局限性，传统的基于2D图像的微手势识别由于没有深度信息而受到精度低的困扰。在本文中，我们提出了一种基于全息3D成像传感器的创新3D微手势识别系统。由于缺少全息3D数据集，因此创建了一个完整的全息3D微手势（HoMG）数据库，并用于开发鲁棒的3D微手势识别方法。然后，提出了一种快速算法从一个全息图像中提取多视点图像。此外，我们将具有基于注意力的残差块的CNN模型应用于每个视点图像，以提高算法性能。最后，将装袋分类树决策级融合应用于预测的组合。实验结果表明，所提出的方法优于现有方法，并且比现有方法具有更高的准确性。