WiFi-ID: Human Identification Using WiFi Signal

Prior research has shown the potential of device-free WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual's gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. We implement WiFi-ID on commercial off-the-shelf devices. We conduct extensive experiments to demonstrate that our system can uniquely identify people with average accuracy of 93% to 77% from a group of 2 to 6 people, respectively. We envisage that this technology can find many applications in small office or smart home settings.

[1]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Xu Chen,et al.  Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi , 2015, MobiHoc.

[3]  Heikki Ailisto,et al.  Identifying users of portable devices from gait pattern with accelerometers , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[4]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[5]  Wei Wang,et al.  Understanding and Modeling of WiFi Signal Based Human Activity Recognition , 2015, MobiCom.

[6]  Wen Hu,et al.  Radio-based device-free activity recognition with radio frequency interference , 2015, IPSN.

[7]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Frans C. A. Groen,et al.  Feature-based human motion parameter estimation with radar , 2008 .

[9]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[10]  Emmanuel,et al.  Using machine learning for real-time activity recognition and estimation of energy expenditure , 2008 .

[11]  David Wetherall,et al.  Tool release: gathering 802.11n traces with channel state information , 2011, CCRV.

[12]  Rama Chellappa,et al.  Gait Analysis for Human Identification , 2003, AVBPA.

[13]  Parth H. Pathak,et al.  Analyzing Shopper's Behavior through WiFi Signals , 2015, WPA@MobiSys.

[14]  Tingting Mu,et al.  Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals , 2012, Journal of The Royal Society Interface.

[15]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[16]  Tapio Seppänen,et al.  Recognizing human motion with multiple acceleration sensors , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[17]  Karim Faez,et al.  Human Identification Based on Gait , 2008 .

[18]  Joanna Verran,et al.  A method for monitoring substratum hygiene using a complex soil: the human fingerprint , 2006 .

[19]  Wei Wang,et al.  Keystroke Recognition Using WiFi Signals , 2015, MobiCom.

[20]  Yingzi Eliza Du.  Review of iris recognition: cameras, systems, and their applications , 2006 .

[21]  Irena Orovic,et al.  A new approach for classification of human gait based on time-frequency feature representations , 2011, Signal Process..

[22]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[23]  Edward J. Delp,et al.  A New Human Identification Method: Sclera Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[24]  L. Talbi,et al.  A Conducting Cylinder for Modeling Human Body Presence in Indoor Propagation Channel , 2007, IEEE Transactions on Antennas and Propagation.

[25]  Li Sun,et al.  WiDraw: Enabling Hands-free Drawing in the Air on Commodity WiFi Devices , 2015, MobiCom.

[26]  Shaogang Gong,et al.  Audio- and Video-based Biometric Person Authentication , 1997, Lecture Notes in Computer Science.

[27]  Chen Wang,et al.  Human Identification Using Temporal Information Preserving Gait Template , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Shaojie Tang,et al.  Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals , 2014, 2014 IEEE Real-Time Systems Symposium.

[29]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing - The Sparse Way, 3rd Edition , 2008 .

[30]  Yunhao Liu,et al.  From RSSI to CSI , 2013, ACM Comput. Surv..