论文标题
通过心率，步态和呼吸数据对可穿戴设备使用者的连续身份验证
Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data
论文作者
论文摘要
私人信息的安全正在成为一个日益数字化社会的基石。当用户充斥着密码和引脚时，这些金标准的显式身份验证变得越来越流行和有价值。最近基于生物识别的身份验证方法（例如面部或手指识别）由于其准确性较高而变得流行。但是，这些基于硬二的系统需要具有功能强大的传感器和身份验证模型的专用设备，这些设备通常仅限于大多数市场可穿戴设备。尽管如此，市场可穿戴设备仍在收集用户的各种私人信息，并已成为生活中不可或缺的一部分：访问汽车，银行帐户等。因此，时间需要使用较不明显的软化数据，可从现代市场可穿戴设备中获得无负担的隐式身份验证机制，以使可穿戴设备可穿戴。在这项工作中，我们介绍了使用心率，步态和呼吸音频信号的可穿戴设备的基于上下文的软测验证系统。从我们使用“一对一”验证的详细分析中，我们发现具有$ k = 2 $的较轻$ k $ - 最近的邻居（$ k $ -nn）模型可以获得平均准确性$ 0.93 \ pm 0.06 $，$ f_1 $ $ f_1 $ $ 0.93 工作。
The security of private information is becoming the bedrock of an increasingly digitized society. While the users are flooded with passwords and PINs, these gold-standard explicit authentications are becoming less popular and valuable. Recent biometric-based authentication methods, such as facial or finger recognition, are getting popular due to their higher accuracy. However, these hard-biometric-based systems require dedicated devices with powerful sensors and authentication models, which are often limited to most of the market wearables. Still, market wearables are collecting various private information of a user and are becoming an integral part of life: accessing cars, bank accounts, etc. Therefore, time demands a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from modern market wearables. In this work, we present a context-dependent soft-biometric-based authentication system for wearables devices using heart rate, gait, and breathing audio signals. From our detailed analysis using the "leave-one-out" validation, we find that a lighter $k$-Nearest Neighbor ($k$-NN) model with $k = 2$ can obtain an average accuracy of $0.93 \pm 0.06$, $F_1$ score $0.93 \pm 0.03$, and {\em false positive rate} (FPR) below $0.08$ at 50\% level of confidence, which shows the promise of this work.
