论文标题
强大的人身份证：一种基于WiFi愿景的方法
Robust Person Identification: A WiFi Vision-based Approach
论文作者
论文摘要
人员重新识别（RE-ID）变得越来越重要，因为它支持广泛的安全应用程序。传统人士重新ID主要依赖于基于光学摄像头的系统，由于人们的外观，闭塞和人类姿势的外观变化，这会产生几个限制。在这项工作中，我们建议一个基于WiFi视觉的系统，即3D-ID，用于3D空间中的人。我们的系统利用WiFi和深度学习的进步来帮助WiFi设备看到，识别和认可人员。特别是，我们利用了下一代WiFi设备上的多个天线和信号反射的2D AOA估计，以使WiFi能够在物理环境中可视化一个人。然后，我们利用深度学习将人的可视化数字化成3D身体表示形式，并为人重新ID提取静态体形和动态步行模式。我们在各种室内环境下的评估结果表明，3D-ID系统的总排名1精度为85.3％。结果还表明，我们的系统对各种攻击具有抵抗力。因此，提出的3D-ID非常有前途，因为它可以增加或补充基于相机的系统。
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.
