Home > Research > Publications & Outputs > Gate-ID: WiFi-based Human Identification Irresp...


Text available via DOI:

View graph of relations

Gate-ID: WiFi-based Human Identification Irrespective of Walking Directions in Smart Home

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Jin Zhang
  • Bo Wei
  • Fuxiang Wu
  • Limeng Dong
  • Wen Hu
  • Salil S Kanhere
  • Chengwen Luo
  • Shui Yu
  • Jun Cheng
<mark>Journal publication date</mark>1/05/2021
<mark>Journal</mark>IEEE Internet of Things Journal
Issue number9
Number of pages15
Pages (from-to)7610-7624
Publication StatusPublished
Early online date26/11/20
<mark>Original language</mark>English


Research has shown the potential of device-free WiFi sensing for human identification. Each and every human has a unique gait and prior works suggest WiFi devices are able to capture the unique signature of a person’s gait. In this article, we show for the first time that the monitored gait could be inconsistent and have mirror-like perturbations when individuals walk through WiFi devices in different directions, provided that the WiFi antenna array is horizontal to the walking path. Such inconsistent mirrored patterns are to negatively affect the uniqueness of gait and accuracy of human identification. Therefore, we propose a system called Gate-ID for accurately identifying individuals’ identities irrespective of different walking directions. Gate-ID employs theoretical communication model and real measurements to demonstrate that antenna array orientations and walking directions contribute to the mirror-like patterns in WiFi signals. A novel heuristic algorithm is proposed to infer individual’s walking directions. A set of methods are employed to extract and augment the representative spatial–temporal features of gait and enable the system performing irrespective of walking directions. We further propose a novel attention-based deep learning model that fuses various weighted features and ignores ineffective noises to uniquely identify individuals. We implement Gate-ID on commercial off-the-shelf devices. Extensive experiments demonstrate that our system can uniquely identify people with average accuracy of 90.7%–75.7% from a group of 6–20 people, respectively, and improve the accuracy by 12.5%–43.5% compared with baselines.