User Reidentification Through mmWave Radio Imaging

User re-identification (Re-ID) plays a crucial role in the research fields of wireless sensing and Internet of things. However, existing vision-based Re-ID approaches often face privacy concerns and are susceptible to variations in lighting conditions. In response to these challenges, in this paper, we propose a novel radio imaging based user Re-ID scheme with a commercial off-the-shelf (COTS) millimeter-wave (mmWave) radar, called RImID. RImID capitalizes on radar signals to initially track individuals in motion. To achieve high-resolution images of tracked subjects, we propose an advanced mmWave Inverse Synthetic Aperture Radar (ISAR) imaging algorithm, which incorporates focusing and nonlinear motion compensation techniques. Importantly, as individuals exhibit unique body movement patterns, RImID captures these distinctions and encodes them within the radio images. Subsequently, RImID utilizes a well-designed convolutional neural network (CNN) that takes these radio images as inputs to identify each registered person. We have implemented RImID using a mmWave radar, specifically the TI IWR1843BOOST, and have collected a comprehensive dataset of over 25.6k mmWave frames for CNN training. Our experimental results confirm the effectiveness of RImID, achieving a remarkable recognition accuracy exceeding 98 percent for 10 individuals and 93 percent for 30 individuals.