Human identification plays an important role in human-computer interaction, and numerous methods have been proposed, e.g., face recognition, gait recognition, fingerprint identification, etc. While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages Wi-Fi signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding Wi-Fi signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of Wi-Fi. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform-based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9% to 94.5% when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.
FreeSense combined Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT), and Dynamic Time Warping (DTW) to achieve a nine-user human identification REF .
FreeSense: Indoor Human Identification with Wi-Fi Signals
2016 IEEE Global Communications Conference (GLOBECOM)
2016 IEEE Global Communications Conference (GLOBECOM)
Computer Science