Toward Open-World-Aware User Authentication Based on Human Bodies Using mmWave Signals

Junlin Yang, Jiadi Yu, Linghe Kong, Yanmin Zhu, Hong-Ning Dai

Abstract: User authentication is evolving with expanded applications and innovative techniques. New authentication approaches utilize RF signals to sense specific human characteristics, 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 generate 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. Next, fine-grained body features are extracted from these environment-independent and posture-independent human silhouette images using a metric learning model. To eliminate potential security threats that arise from unauthorized users, OpenAuth synthesizes data placeholders for enhancing unauthorized user identification. Finally, a k-NN-based authentication model is constructed 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.

External IDs:dblp:journals/tmc/YangYKZD25