Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking

Sarah S. Jasim; Alia K. Abdul Hassan; Scott Turner

doi:10.14500/aro.10928

Sarah S. Jasim Department of IT, Technical College of Management-Baghdad, Middle Technical University, Baghdad, Iraq https://orcid.org/0000-0002-1237-147X
Alia K. Abdul Hassan Department of Computer Science, University of Technology, Baghdad, Iraq https://orcid.org/0000-0002-6835-8872
Scott Turner 3 Director of Computing, School of Engineering, Design, and Technology, Church Christ Church University, Kent, United Kingdom https://orcid.org/0000-0003-2735-3220

Keywords: Artificial neural network, Drowsiness, Feature extraction, Gray wolf optimizer, Normalization, Segmentation

Abstract

It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%.

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Author Biographies

Sarah S. Jasim, Department of IT, Technical College of Management-Baghdad, Middle Technical University, Baghdad, Iraq

is a Lecturer at the Computer Sciences Department/Department of IT, Technical college of Management-Baghdad, Middle Technical University. She got a B.Sc. degree in Computer Sciences/AL Yarmouk University College in 2004. MSc. degree in Computer Sciences/University of Technology in 2008, and a degree in Student of Ph.D. in Computer Sciences,
University of Technology. Her research interests are in Data Mining, Document Recognition, Electronic Management, and Computer security.

Alia K. Abdul Hassan, Department of Computer Science, University of Technology, Baghdad, Iraq

is a Professor at the Computer Sciences Department/University of Technology, Baghdad, Iraq. She got a B.Sc. degree in Computer Sciences/University of Technology in 1993. MSc. degree in Computer Sciences/University of Technology, 1999, and a Ph.D. degree in Computer Sciences/University of Technology in 2004. Her research interests are in soft computing, green computing, AI, Data Mining, Software engineering, Electronic Management, and Computer
security. Dr. Alia is the Position of Dean of the Computer Science Department since Feb 2019 till now.

Scott Turner, 3 Director of Computing, School of Engineering, Design, and Technology, Church Christ Church University, Kent, United Kingdom

Scott Turner is a Director of Computing at Section of Computing, School of Engineering, Design and Technology, Canterbury Christ Church University, Kent, UK. He got a B.Eng degree in Electronics Engineering, University of Hull, UK, M.Sc. degree in Biomedical Instrumentation Engineering, University of Dundee, UK and the Ph.D. degree in applying evolutionary algorithms to evoked potentials, University of Leicester, UK. His research interests are in applies machine learning; pedagogy in computing. Dr. Turner is a member of Institution of Electrical and Electronics Engineers; Institute of Engineering and Technology; British Computer Society; Association of Computing Machines; Fellow of the Royal Society for the Encouragement of Arts, Manufactures and Commerce

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Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking

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