2018/10/06 by Fei Wang, Wang, Fei, Jinsong Han +5 · 2 citations
Computer Science · Engineering · Mathematics · #Computer science #Computer security #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Gait #Gait Recognition and Analysis #Human–computer interaction #Identification (biology) #Indoor and Outdoor Localization Technologies #Internet of Things #Limiting #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Matching (statistics) #Overhead (engineering) #Real-time computing #Robustness (evolution) #Signal Processing (eess.SP) #Speech and Audio Processing #cs.LG #eess.SP #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.04106
published in arXiv (Cornell University) (Cornell University) · accepted by GLOBECOM 2019
openalex publication_date 2018/10/06 · arxiv created 2019/07/18 · arxiv updated 2019/07/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness. Most previous work applies gaits extracted from WiFi distortions caused by the person walking to achieve the identification. However, to extract useful gait, a person must walk along a pre-defined path for several meters, which requires user high collaboration and increases identification time overhead, thus limiting use scenarios. Moreover, gait based work has severe shortcoming in identification performance, especially when the user volume is large. In order to eliminate the above limitations, in this paper, we present an operation-free person identification system, namely WiPIN, that requires least user collaboration and achieves good performance. WiPIN is based on an entirely new insight that Wi-Fi signals would carry person body information when propagating through the body, which is potentially discriminated for person identification. Then we demonstrate the feasibility on commodity off-the-shelf Wi-Fi devices by well-designed signal pre-processing, feature extraction, and identity matching algorithms. Results show that WiPIN achieves 92% identification accuracy over 30 users, high robustness to various experimental settings, and low identifying time overhead, i.e., less than 300ms.