[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 :
Ingénierie électrique & électronique
Auteur, co-auteur :
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
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles
Titre traduit :
[en] Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles
Date de publication/diffusion :
05 mai 2022
Titre du périodique :
Wireless Communications and Mobile Computing
ISSN :
1530-8669
eISSN :
1530-8677
Maison d'édition :
John Wiley & Sons, Hoboken, Etats-Unis - New Jersey
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