Continuous Authentication Through Finger Gesture Interaction for Smart Homes Using WiFi
Continuous Authentication Through Finger Gesture Interaction for Smart Homes Using WiFi
复制标题
使用 WiFi 的智能家居通过手指手势交互进行持续身份验证
DOI：
10.1109/tmc.2020.2994955
复制
发表时间：
2021-11-01
影响因子：
7.9
通讯作者：
Tang, Feilong
中科院分区：
文献类型：
--
作者：
Kong, Hao;Lu, Li;Tang, Feilong
智能家居的发展提升了用户认证的概念，不仅保护了用户的隐私，还为用户提供了个性化的服务。沿着这个方向，我们提出通过广泛部署的WiFi基础设施，将用户认证与用户和智能家电之间的人机交互集成在一起，这是非侵入性和无设备的。在本文中，我们提出了手指通行证，利用周围的WiFi信号的信道状态信息（CSI），通过手指手势在智能家居中不断认证用户。Finger Pass将用户身份验证过程分为登录和交互两个阶段，同时实现高身份验证准确性和低响应延迟。在登录阶段，我们开发了一种基于深度学习的方法来提取手指手势的行为特征，以实现高度准确的用户识别。在交互阶段，为了提供真实的连续身份验证以获得满意的用户体验，我们设计了一种轻量级的身份验证机制，在每次手指手势交互过程中对用户身份进行连续验证。在真实的环境下的实验表明，Finger Pass认证准确率在域内达到90.6%，跨域达到87.6%，交互响应时间为186.6 ms。
The development of smart homes has advanced the concept of user authentication to not only protecting user privacy but also facilitating personalized services to users. Along this direction, we propose to integrate user authentication with human-computer interactions between users and smart household appliances through widely-deployed WiFi infrastructures, which is non-intrusive and device-free. In this paper, we propose Finger Pass which leverages channel state information (CSI) of surrounding WiFi signals to continuously authenticate users through finger gestures in smart homes. Finger Pass separates the user authentication process into two stages, login and interaction, to achieve high authentication accuracy and low response latency simultaneously. In the login stage, we develop a deep learning-based approach to extract behavioral characteristics of finger gestures for highly accurate user identification. For the interaction stage, to provide continuous authentication in real time for satisfactory user experience, we design a verification mechanism with lightweight classifiers to continuously authenticate the user's identity during each interaction of finger gestures. Experiments in real environments show that Finger Pass can achieve the authentication accuracies of 90.6 percent under in-domain scenarios and 87.6 percent under cross-domain scenarios, as well as 186.6 ms response time during interactions.