  Human identification plays an important role in human-computer interaction.
There have been numerous methods proposed for human identification (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
WIFI 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 WIFI 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 WIFI.
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.
