论文标题
对手的帮助：基于基于梯度的无设备域独立的手势识别
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition
论文作者
论文摘要
基于无线信号的手势识别促进了VR游戏，智能家居等的发展。但是，传统方法遭受了域间隙的影响。当在一个域中训练识别模型但在另一个域中使用时，就会发生低识别精度。尽管已经提出了一些解决方案，例如对抗性学习，转移学习和身体坐标速度概况来实现跨域识别，但这些解决方案或多或少都有缺陷。在本文中，我们定义了域间隙的概念，然后提出了一个更有希望的解决方案，即DI，以消除域间隙并进一步实现与域无关的手势识别。 DI利用梯度图的标志图作为域隙消除器，以提高识别精度。我们对十个领域和十个手势进行实验。实验结果表明，DI可以实现KNN，SVM和CNN的识别精度为87.13％，90.12％和94.45％，这表现优于现有解决方案。
Wireless signal-based gesture recognition has promoted the developments of VR game, smart home, etc. However, traditional approaches suffer from the influence of the domain gap. Low recognition accuracy occurs when the recognition model is trained in one domain but is used in another domain. Though some solutions, such as adversarial learning, transfer learning and body-coordinate velocity profile, have been proposed to achieve cross-domain recognition, these solutions more or less have flaws. In this paper, we define the concept of domain gap and then propose a more promising solution, namely DI, to eliminate domain gap and further achieve domain-independent gesture recognition. DI leverages the sign map of the gradient map as the domain gap eliminator to improve the recognition accuracy. We conduct experiments with ten domains and ten gestures. The experiment results show that DI can achieve the recognition accuracies of 87.13%, 90.12% and 94.45% on KNN, SVM and CNN, which outperforms existing solutions.
