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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.<\/jats:p>","DOI":"10.1145\/3550306","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T14:54:27Z","timestamp":1662562467000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":46,"title":["MetaGanFi"],"prefix":"10.1145","volume":"6","author":[{"given":"Jin","family":"Zhang","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}]},{"given":"Zhuangzhuang","family":"Chen","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}]},{"given":"Chengwen","family":"Luo","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}]},{"given":"Bo","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computing and Communications, Lancaster University, Lancaster, United Kingdom"}]},{"given":"Salil S.","family":"Kanhere","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"}]},{"given":"Jianqiang","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"TransferSense: towards environment independent and one-shot wifi sensing. 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