HumanFi: WiFi-Based Human Identification Using Recurrent Neural Network

• Published in: 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) pp. 640 - 647
• Main Authors: Ming, Xingxia, Feng, Hongwei, Bu, Qirong, Zhang, Jing, Yang, Gang, Zhang, Tuo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2019
• Subjects: Antennas; Biometrics (access control); Channel State Information; Feature extraction; Gait Biometrics; Human Identification; Legged locomotion; LSTM; Receivers; Recurrent neural networks; WiFi; Wireless fidelity
• DOI: 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00146

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.