手势
计算机科学
手势识别
认证(法律)
不变(物理)
人机交互
人工智能
计算机安全
数学
数学物理
作者
Hao Kong,Li Lü,Jiadi Yu,Yanmin Zhu,Feilong Tang,Yichao Chen,Linghe Kong,Feng Lyu
标识
DOI:10.1109/infocom48880.2022.9796740
摘要
With the development of smart indoor environments, user authentication becomes an essential mechanism to support various secure accesses. Although recent studies have shown initial success on authenticating users with human activities or gestures using WiFi, they rely on predefined body gestures and perform poorly when meeting undefined body gestures. This work aims to enable WiFi-based user authentication with undefined body gestures rather than only predefined body gestures, i.e., realizing a gesture-independent user authentication. In this paper, we first explore physiological characteristics underlying body gestures, and find that statistical distributions under WiFi signals induced by body gestures can exhibit invariant individual uniqueness unrelated to specific body gestures. Inspired by this observation, we propose a user authentication system, which utilizes WiFi signals to identify individuals in a gesture-independent manner. Specifically, we design an adversarial learning-based model, which suppresses specific gesture characteristics, and extracts invariant individual uniqueness unrelated to specific body gestures, to authenticate users in a gesture-independent manner. Extensive experiments in indoor environments show that the proposed system is feasible and effective in gesture-independent user authentication.
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