Name
Abstract | ||
|---|---|---|
Person identification is critical for sensitive applications such as system login/unlock, access control and payment. In this paper, we present an operation-free person identification system, namely WiPIN, that identifies biometric features of users using Wi-Fi signals. Our approach is based on an entirely new insight that different persons have distinct effects, including the absorption and reflection, on the Wi-Fi signals. We show that through effective signal processing and feature extraction/matching designs, the Channel State Information (CSI) used in recent Wi-Fi protocols can be utilized for person identification without requiring any collaborative operations, such as wiping, walking, or speaking. We theoretically analyzed the interaction between the human body and Wi-Fi Signals via an interactive model. We proposed a mapping rule between variation patterns of Wi-Fi signals and human biologic features, and demonstrated the feasibility of establishing CSI based person identifiers. We conducted extensive experiments over commodity off-the-shelf Wi-Fi devices. The results show WiPIN achieves 92% accuracy in person identification over a group of 30 users, with sufficient robustness to environment noises. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Signal Processing | Data mining,Signal processing,Identifier,Computer science,Login,Robustness (computer science),Feature extraction,Access control,Biometrics,Channel state information |
DocType | Volume | Citations |
Journal | abs/1810.04106 | 1 |
PageRank | References | Authors |
0.35 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Wang | 1 | 241 | 51.35 |
| Jinsong Han | 2 | 876 | 63.13 |
| Ziyi Dai | 3 | 1 | 1.02 |
| Han Ding | 4 | 499 | 78.16 |
| Dong Huang | 5 | 163 | 14.20 |