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Title Humanfi: Wifi-Based Human Identification Using Recurrent Neural Network
ID_Doc 29674
Authors Ming X.; Feng H.; Bu Q.; Zhang J.; Yang G.; Zhang T.
Year 2019
Published Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
DOI http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00146
Abstract Because of the uniqueness of human gait, the WiFi signal reflected by a walking person can generate a distinctive variation in the received WiFi channel state information (CSI). In this paper, we present a new passive human identification method named HumanFi based on fine-grained gait patterns captured by commercial WiFi device and long short term memory network (LSTM). Firstly, CSI measurements are collected by a commercial WiFi device, and then a buffer and filtering mechanism-based gait detection algorithm is proposed to solve the effects of short-term anomalous fluctuation. After that, a recurrent neural network, LSTM, is used to identify different people by discriminating the temporal characteristics of automatically extracted human gait features. We evaluated the proposed HumanFi using a dataset with 1920 gait instances collected from 24 human subjects walking in two different scenes. Experimental results showed that HumanFi achieved more than 96% human identification accuracy, which demonstrated the good performance of HumanFi on non-intrusive human identification tasks. © 2019 IEEE.
Author Keywords Channel state information; Gait biometrics; Human identification; Lstm; WiFi


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