  Human identification is a key requirement for many applications in everyday
life, such as personalized services, automatic surveillance, continuous
authentication, and contact tracing during pandemics, etc. This work studies
the problem of cross-modal human re-identification (ReID), in response to the
regular human movements across camera-allowed regions (e.g., streets) and
camera-restricted regions (e.g., offices) deployed with heterogeneous sensors.
By leveraging the emerging low-cost RGB-D cameras and mmWave radars, we propose
the first-of-its-kind vision-RF system for cross-modal multi-person ReID at the
same time. Firstly, to address the fundamental inter-modality discrepancy, we
propose a novel signature synthesis algorithm based on the observed specular
reflection model of a human body. Secondly, an effective cross-modal deep
metric learning model is introduced to deal with interference caused by
unsynchronized data across radars and cameras. Through extensive experiments in
both indoor and outdoor environments, we demonstrate that our proposed system
is able to achieve ~92.5% top-1 accuracy and ~97.5% top-5 accuracy out of 56
volunteers. We also show that our proposed system is able to robustly
reidentify subjects even when multiple subjects are present in the sensors'
field of view.
