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

Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars