Wihi: WiFi Based Human Identity Identification Using Deep Learning

Abstract

Human identity identification based on channel state information (CSI) using commercial WiFi devices has drawn increasingly attention, and it can be used in many applications such as smart home, intrusion detection, building monitoring, activity recognition, etc. However, most of the existing identity identification approaches are sensitive to the influence of random noise derived from indoor environments, and thus their identification accuracies are far from satisfactory. In the present paper, a device-free CSI based human identity identification approach using deep learning (Wihi) is proposed. Wihi mainly utilizes three key techniques to identify different people. Firstly, to eliminate the influence of the random noise, discrete wavelet transform (DWT) strategy is introduced to denoise raw CSI data by leveraging signal decomposition. Secondly, in order to characterize human’s gaits profoundly, several representative features are exploited from different statistical profiles, including channel power distribution in time domain (CPD), time-frequency analysis (TFA), and energy distribution in different frequency bands (ED). Thirdly, a recurrent neural network (RNN) model with long short-term memory (LSTM) blocks is employed to learn the representative gait features extracted above and encode temporal information for realizing human identity identification. The proof-of-concept prototype of the proposed Wihi approach is implemented on a set of commercial WiFi devices, and multiple comprehensive experiments have been carried out to evaluate the performance of identity identification. The experimental results confirm that the proposed Wihi can achieve a satisfactory performance compared with some state-of-the-art approaches.