Robust Person Identification: A WiFi Vision-based Approach
This addresses the problem of person re-identification for security applications by offering a robust alternative to camera-based systems, though it is incremental as it builds on existing WiFi and deep learning techniques.
The paper tackles person re-identification by proposing a WiFi vision-based system called 3D-ID, which uses WiFi signals and deep learning to visualize and identify people in 3D space, achieving an overall rank-1 accuracy of 85.3% in indoor environments.
Person re-identification (Re-ID) has become increasingly important as it supports a wide range of security applications. Traditional person Re-ID mainly relies on optical camera-based systems, which incur several limitations due to the changes in the appearance of people, occlusions, and human poses. In this work, we propose a WiFi vision-based system, 3D-ID, for person Re-ID in 3D space. Our system leverages the advances of WiFi and deep learning to help WiFi devices see, identify, and recognize people. In particular, we leverage multiple antennas on next-generation WiFi devices and 2D AoA estimation of the signal reflections to enable WiFi to visualize a person in the physical environment. We then leverage deep learning to digitize the visualization of the person into 3D body representation and extract both the static body shape and dynamic walking patterns for person Re-ID. Our evaluation results under various indoor environments show that the 3D-ID system achieves an overall rank-1 accuracy of 85.3%. Results also show that our system is resistant to various attacks. The proposed 3D-ID is thus very promising as it could augment or complement camera-based systems.