[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%.
Disciplines :
Electrical & electronics engineering
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
External co-authors :
yes
Language :
English
Title :
Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles
Alternative titles :
[en] Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles
Publication date :
05 May 2022
Journal title :
Wireless Communications and Mobile Computing
ISSN :
1530-8669
eISSN :
1530-8677
Publisher :
John Wiley & Sons, Hoboken, United States - New Jersey
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