Ebenezer Essel
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ebenezer Essel has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Fatema A. Albalooshi
College of Information Technology, University of Bahrain, Sakhir P.O. Box 32038, Bahrain
1 shared publication - Fred Lacy
Department of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USA
1 shared publication - Wael Elmedany
University of Bahrain
1 shared publication - Yasser Ismail
Department of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70813, USA
1 shared publication
Latest Publications
- Open Access
- 24 December 2024
- Published by MDPI AG in Applied Sciences
- Vol. 15 (1) , 20
Drowsiness Detection in Drivers Using Facial Feature Analysis
Drowsiness has been recognized as a leading factor in road accidents worldwide. Despite considerable research in this area, this paper aims to improve the precision of drowsiness detection specifically for long-haul travel by employing the Dlib-based facial feature detection algorithm. This study proposes two algorithms: a static and adaptive frame threshold. Both approaches utilize eye closure ratio (ECR) and mouth aperture ratio (MAR) parameters to determine the driver’s level of drowsiness. The static threshold method issues a warning when the ECR and/or MAR values reach specific thresholds. In this method, the ECR threshold is established at 0.15 and the MAR threshold at 0.4. The static threshold method demonstrated an accuracy of 89.4% and a sensitivity of 96.5% using 1000 images. The adaptive frame threshold algorithm uses a counter to monitor the number of consecutive frames that meet the drowsiness criteria before triggering a warning. Additionally, the number of consecutive frames required is adjusted dynamically over time to enhance detection accuracy and more accurately indicate a state of drowsiness. The adaptive frame threshold algorithm was tested using four 30 min videos, from a publicly available dataset achieving a maximum accuracy of 98.2% and a sensitivity of 64.3% with 500 images.
- 24 December 2024
- Published by MDPI AG in Applied Sciences
- Vol. 15 (1) , 20
Open AccessDrowsiness Detection in Drivers Using Facial Feature Analysis
Drowsiness has been recognized as a leading factor in road accidents worldwide. Despite considerable research in this area, this paper aims to improve the precision of drowsiness detection specifically for long-haul travel by employing the Dlib-based facial feature detection algorithm. This study proposes two algorithms: a static and adaptive frame threshold. Both approaches utilize eye closure ratio (ECR) and mouth aperture ratio (MAR) parameters to determine the driver’s level of drowsiness. The static threshold method issues a warning when the ECR and/or MAR values reach specific thresholds. In this method, the ECR threshold is established at 0.15 and the MAR threshold at 0.4. The static threshold method demonstrated an accuracy of 89.4% and a sensitivity of 96.5% using 1000 images. The adaptive frame threshold algorithm uses a counter to monitor the number of consecutive frames that meet the drowsiness criteria before triggering a warning. Additionally, the number of consecutive frames required is adjusted dynamically over time to enhance detection accuracy and more accurately indicate a state of drowsiness. The adaptive frame threshold algorithm was tested using four 30 min videos, from a publicly available dataset achieving a maximum accuracy of 98.2% and a sensitivity of 64.3% with 500 images.