Abstract
The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. As the technology advances and communication between humans and machines becomes more complex, HCI systems should also be scaled accordingly in order to accommodate the introduced complexities. In this paper, we propose a methodology to scale hand gestures by forming them with predefined gesture-phonemes, and a convolutional neural network (CNN) based framework to recognize hand gestures by learning only their constituents of gesture-phonemes. The total number of possible hand gestures can be increased exponentially by increasing the number of used gesture-phonemes. For this objective, we introduce a new benchmark dataset named Scaled Hand Gestures Dataset (SHGD) with only gesture-phonemes in its training set and 3-tuples gestures in the test set. In our experimental analysis, we achieve to recognize hand gestures containing one and three gesture-phonemes with an accuracy of 98.47% (in 15 classes) and 94.69% (in 810 classes), respectively. Our dataset, code and pretrained models are publicly available.
Abstract (translated)
手势的使用为人机交互（HCI）系统提供了一种自然的替代方法，以替代繁琐的接口设备。随着技术的进步和人与机器之间的通信变得更加复杂，HCI系统也应该相应地进行扩展，以适应引入的复杂性。在本文中，我们提出了一种通过预先定义的手势音素来形成手势来缩放手势的方法，以及一个基于卷积神经网络（CNN）的框架，通过只学习手势音素的成分来识别手势。通过增加使用的手势音素的数量，可能的手势总数可以成倍增加。为了实现这一目标，我们引入了一个新的基准数据集，称为缩放手势数据集（shgd），它的训练集中只包含手势音素，测试集中包含3元组手势。在我们的实验分析中，我们实现了识别包含一个和三个手势音素的手势，准确率分别为98.47%（15个等级）和94.69%（810个等级）。我们的数据集、代码和预培训模型是公开的。
URL
https://arxiv.org/abs/1905.04225
