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

Zhang, Jin and Wei, Bo and Wu, Fuxiang and Dong, Limeng and Hu, Wen and Kanhere, Salil S and Luo, Chengwen and Yu, Shui and Cheng, Jun (2021) Gate-ID: WiFi-based Human Identification Irrespective of Walking Directions in Smart Home. IEEE Internet of Things Journal, 8 (9). pp. 7610-7624. ISSN 2327-4662

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Internet of Things Journal
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1705
Subjects:
ID Code:
171686
Deposited By:
Deposited On:
14 Jun 2022 14:00
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2022 11:33