OpenAuth: Human Body-Based User Authentication Using mmWave Signals in Open-World Scenarios

User authentication is evolving with expanded application scenarios and innovative techniques. New authentication approaches utilize RF signals to sense specific human behaviors and characteristics, such as faces, specific gestures, etc., offering a contactless and nonintrusive solution. However, these RF signal-based methods struggle with challenges in open-world scenarios, i.e., dynamic environments, daily behaviors with unrestricted postures, and identification of unauthorized users with security threats. In this paper, we present an open-world user authentication system, OpenAuth, which leverages a commercial off-the-shelf (COTS) mmWave radar to sense unrestricted human postures and behaviors for identifying individuals. First, OpenAuth utilizes a MUSIC-based neural network imaging model to eliminate environmental clutter and generates environment-independent human silhouette images. Then, the human silhouette images are normalized to consistent topological structures of human postures, ensuring robustness against unrestricted human postures. Based on the environment-independent and posture-independent human silhouette images, OpenAuth further extracts fine-grained body features through a metric learning model for user authentication. To eliminate potential security threats that arise from frequent accesses by unauthorized users, OpenAuth synthesizes data placeholders for enhancing the applicability of unauthorized user identification. Finally, a k-NN-based authentication model is constructed based on the extracted body features to authenticate users' identities. Experiments in real environments show that the proposed OpenAuth achieves an average authentication accuracy of 93.4 % and false acceptance rate (FAR) of 1.8% in open-world scenarios.