  Wi-Fi signals-based person identification attracts increasing attention in
the booming Internet-of-Things era mainly due to its pervasiveness and
passiveness. Most previous work applies gaits extracted from WiFi distortions
caused by the person walking to achieve the identification. However, to extract
useful gait, a person must walk along a pre-defined path for several meters,
which requires user high collaboration and increases identification time
overhead, thus limiting use scenarios. Moreover, gait based work has severe
shortcoming in identification performance, especially when the user volume is
large. In order to eliminate the above limitations, in this paper, we present
an operation-free person identification system, namely WiPIN, that requires
least user collaboration and achieves good performance. WiPIN is based on an
entirely new insight that Wi-Fi signals would carry person body information
when propagating through the body, which is potentially discriminated for
person identification. Then we demonstrate the feasibility on commodity
off-the-shelf Wi-Fi devices by well-designed signal pre-processing, feature
extraction, and identity matching algorithms. Results show that WiPIN achieves
92% identification accuracy over 30 users, high robustness to various
experimental settings, and low identifying time overhead, i.e., less than
300ms.

Signal Processing;Machine Learning;Machine Learning