RFPass: Towards Environment-Independent Gait-based User Authentication Leveraging RFID

Gait-based user authentication schemes have been widely explored because of their ability of non-invasive sensing and avoid replay attacks. However, existing gait-based user authentication methods are environment-dependent. In this paper, we present an environment-independent gait-based user authentication system, RFPass, which can identify different individuals leveraging RFID signals. Specifically, we find that Doppler shift of RF signals can describe environment-independent gait features for different individuals. In RFPass, when a user walks through the RFPass system, RF signals are first collected by a deployed RFID tag array. Then, RFPass removes environmental interference from the collected RF signals through a proposed Multipath Direction of arrival (DoA) Signal Select (MDSS) algorithm. Next, we construct an environment-independent gait profile to describe the user's walking movements. Afterward, environment-independent gait features are extracted by a proposed CNN-RNN model. Based on the extracted gait features, a trained model is constructed for user authentication and spoofer detection. Extensive experiments in different real environments demonstrate that RFPass can achieve environment-independent gait-based user authentication.