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Lookup NU author(s): Dr Bo WeiORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Association for Computing Machinery, 2022.
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© 2022 ACM.Human has an unique gait and prior works show increasing potentials in using WiFi signals to capture the unique signature of individuals' gait. However, existing WiFi-based human identification (HI) systems have not been ready for real-world deployment due to various strong assumptions including identification of known users and sufficient training data captured in predefined domains such as fixed walking trajectory/orientation, WiFi layout (receivers locations) and multipath environment (deployment time and site). In this paper, we propose a WiFi-based HI system, MetaGanFi, which is able to accurately identify unseen individuals in uncontrolled domain with only one or few samples. To achieve this, the MetaGanFi proposes a domain unification model, CCG-GAN that utilizes a conditional cycle generative adversarial networks to filter out irrelevant perturbations incurred by interfering domains. Moreover, the MetaGanFi proposes a domain-agnostic meta learning model, DA-Meta that could quickly adapt from one/few data samples to accurately recognize unseen individuals. The comprehensive evaluation applied on a real-world dataset show that the MetaGanFi can identify unseen individuals with average accuracies of 87.25% and 93.50% for 1 and 5 available data samples (shot) cases, captured in varying trajectory and multipath environment, 86.84% and 91.25% for 1 and 5-shot cases in varying WiFi layout scenarios, while the overall inference process of domain unification and identification takes about 0.1 second per sample.
Author(s): Zhang J, Chen Z, Luo C, Wei B, Kanhere SS, Li J
Publication type: Article
Publication status: Published
Journal: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Year: 2022
Volume: 6
Issue: 3
Print publication date: 01/09/2022
Online publication date: 07/09/2022
Acceptance date: 02/04/2022
Date deposited: 29/06/2023
ISSN (electronic): 2474-9567
Publisher: Association for Computing Machinery
URL: https://doi.org/10.1145/3550306
DOI: 10.1145/3550306
ePrints DOI: 10.57711/nrgd-4009
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