TY - JOUR

T1 - MetaGanFi

T2 - Cross-Domain Unseen Individual Identification Using WiFi Signals

AU - Zhang, Jin

AU - Chen, Zhuangzhuang

AU - Luo, Chengwen

AU - Wei, Bo

AU - Kanhere, Salil S

AU - Li, Jianqiang

N1 - © ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6, 3, 2022 http://doi.acm.org/10.1145/3550306

PY - 2022/9/7

Y1 - 2022/9/7

N2 - 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.

AB - 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.

U2 - 10.1145/3550306

DO - 10.1145/3550306

M3 - Journal article

VL - 6

SP - 1

EP - 21

JO - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

JF - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

SN - 2474-9567

IS - 3

M1 - 152

ER -