HumanFi: WiFi-Based Human Identification Using Recurrent Neural Network

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.