  Person Re-Identification (ReID) aims to recognize a person-of-interest across
different places and times. Existing ReID methods rely on images or videos
collected using RGB cameras. They extract appearance features like clothes,
shoes, hair, etc. Such features, however, can change drastically from one day
to the next, leading to inability to identify people over extended time
periods. In this paper, we introduce RF-ReID, a novel approach that harnesses
radio frequency (RF) signals for longterm person ReID. RF signals traverse
clothes and reflect off the human body; thus they can be used to extract more
persistent human-identifying features like body size and shape. We evaluate the
performance of RF-ReID on longitudinal datasets that span days and weeks, where
the person may wear different clothes across days. Our experiments demonstrate
that RF-ReID outperforms state-of-the-art RGB-based ReID approaches for long
term person ReID. Our results also reveal two interesting features: First since
RF signals work in the presence of occlusions and poor lighting, RF-ReID allows
for person ReID in such scenarios. Second, unlike photos and videos which
reveal personal and private information, RF signals are more
privacy-preserving, and hence can help extend person ReID to privacy-concerned
domains, like healthcare.
