Abstract
Applications using gesture-based human-computer interface require a new user login method with gestures because it does not have a traditional input method to type a password. However, due to various challenges, existing gesture-based authentication systems are generally considered too weak to be useful in practice. In this paper, we propose a unified user login framework using 3D in-air-handwriting, called FMCode. We define new types of features critical to distinguish legitimate users from attackers and utilize Support Vector Machine (SVM) for user authentication. The features and data-driven models are specially designed to accommodate minor behavior variations that existing gesture authentication methods neglect. In addition, we use deep neural network approaches to efficiently identify the user based on his or her in-air-handwriting, which avoids expansive account database search methods employed by existing work. On a dataset collected by us with over 100 users, our prototype system achieves 0.1% and 0.5% best Equal Error Rate (EER) for user authentication, as well as 96.7% and 94.3% accuracy for user identification, using two types of gesture input devices. Compared to existing behavioral biometric systems using gesture and in-air-handwriting, our framework achieves the state-of-the-art performance. In addition, our experimental results show that FMCode is capable to defend against client-side spoofing attacks, and it performs persistently in the long run. These results and discoveries pave the way to practical usage of gesture-based user login over the gesture interface.
Abstract (translated)
使用基于手势的人机界面的应用程序需要具有手势的新用户登录方法，因为它没有用于键入密码的传统输入方法。然而，由于各种挑战，现有的基于姿势的认证系统通常被认为太弱而不能在实践中有用。在本文中，我们提出了一个使用3D空中手写的统一用户登录框架，称为FMCode。我们定义了对于区分合法用户和攻击者至关重要的新类型功能，并使用支持向量机（SVM）进行用户身份验证。功能和数据驱动模型专门设计用于适应现有手势身份验证方法忽略的微小行为变化。此外，我们使用深度神经网络方法基于他或她的空中手写来有效地识别用户，这避免了现有工作所采用的扩展的帐户数据库搜索方法。在由我们收集的超过100个用户的数据集中，我们的原型系统使用两种类型的手势输入，用户身份验证达到0.1％和0.5％的最佳等错误率（EER），以及用户识别的96.7％和94.3％准确度设备。与使用手势和空中手写的现有行为生物识别系统相比，我们的框架实现了最先进的性能。此外，我们的实验结果表明，FMCode能够抵御客户端欺骗攻击，并且从长远来看它可以持续执行。这些结果和发现为在手势界面上基于手势的用户登录的实际使用铺平了道路。
URL
https://arxiv.org/abs/1808.00130
