Reference : Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation In...

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/54064

Title :	Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles
Language :	English
Translated title :	[en] Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles
Author, co-author :	Akhtar, Zain ul Abidin []
	Rassol, Hafiz Faiz []
	Asif, Muhammad []
	Khan, Wali Ullah [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
	Jaffri, Zain ul Abidin []
Publication date :	5-May-2022
Journal title :	Wireless Communications and Mobile Computing
Publisher :	John Wiley & Sons
Volume :	2022
Peer reviewed :	Yes
Audience :	International
ISSN :	1530-8669
e-ISSN :	1530-8677
City :	Hoboken
Country :	United States - New Jersey
Keywords :	[en] Internet of vehicle ; Face post estimation ; Wifi
Abstract :	[en] Driver’s behavior and gesture recognition are most significant in the emerging next-generation vehicular technology. Driver’s face may provide important cues about his/her attention and fatigue behavior. Therefore, driver’s face pose is one of the key indicators to be considered for automatic driver monitoring system in next-generation Internet of Vehicles (IoV) technology. Driver behavior monitoring is most significant in order to reduce road accidents. This paper aims to address the problem of driver’s attentiveness monitoring using face pose estimation in a nonintrusive manner. The proposed system is based on wireless sensing, leveraging channel state information (CSI) of WiFi signals. In this paper, we present a novel classification algorithm that is based on the combination of support vector machine (SVM) and K nearest neighbor (KNN) to enhance the classification accuracy. Experimental results demonstrate that the proposed device-free wireless implementation can localize a driver’s face very accurately with an average recognition rate of 91:8%.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/54064
DOI :	10.1155/2022/7353080
Other URL :	https://www.hindawi.com/journals/wcmc/2022/7353080/

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