Person Re-Identification Using WiFi Signals

Person re-identification (Re-ID) has become increasingly important as it supports a wide range of security applications. In this work, we propose a WiFi-based person Re-ID system in 3D space, which leverages the advances of WiFi and deep learning to extract the static body shape and dynamic walking patterns to recognize people. In particular, we leverage multiple antennas on WiFi devices to capture signal reflections of the human body and produce a WiFi image of a person. We then leverage deep learning to extract both the static body shape and dynamic walking patterns for person Re-ID. Our evaluation results show that our system achieves an overall rank-1 accuracy of 87.1%.