Paper Info
Title
Wii: Device-Free Passive Identity Identification via WiFi Signals.
Abstract
Human behavior data is the basis of behavior analysis, and usually we need to collect large quantities of data before analysis. Most existing data collection methods are labor intensive works in which the volunteers need to be asked to behave naturally under the monitoring of researchers. Identity identification can be used in passive data collection of human behavior analysis systems in big data. Previous researches show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human's gait is unique from each other like fingerprint and iris. As a result, researchers start to explore the ability of WiFi in human identification. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human's gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract entities' gait features from both time and frequency domain. Based on these features, Wii realizes identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated in a typical indoor scenario. The results indicate that Wii achieves high identification accuracy with low computational cost and has the potential to work in human behavior analysis systems.
Year
Venue
Keywords
2017
IEEE Global Communications Conference
WiFi,channel state information,human gait,human identification,human behavior analysis
Field
DocType
ISSN
Kernel (linear algebra),Frequency domain,Data collection,Computer science,Support vector machine,Feature extraction,Real-time computing,Fingerprint,Big data,Channel state information
Conference
2334-0983
Citations 
PageRank 
References 
2
0.38
0
Authors
6
Name
Order
Citations
PageRank
Jiguang Lv1114.32
Yang Wu26922.62
Dapeng Man32910.54
X. Du42320241.73
Yu Miao51813.21
Mohsen Guizani66456557.44