Sensing Human Gait for Environment-Independent User Authentication Using Commodity RFID Devices

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, and further constructs the environment-independent gait profile. Afterwards, environment-independent gait features are extracted from the constructed gait profile by a proposed CNN-RNN model. Based on the extracted gait features, a hierarchical classifier is trained for user authentication and spoofer detection. Extensive experiments in different real environments demonstrate that
RFPass
can achieve environment-independent gait-based user authentication.