Halim, D., Hanafy, M., Lotfy, Y., Deif, M., Elgohary, R. (2024). Real-time Driver Drowsiness Detection Using Deep Neural Networks. test, 1(2), -. doi: 10.21608/iiis.2024.357785
Daniel Halim; Mariam Hanafy; Youssef Lotfy; Mohanad Deif; Rania Elgohary. "Real-time Driver Drowsiness Detection Using Deep Neural Networks". test, 1, 2, 2024, -. doi: 10.21608/iiis.2024.357785
Halim, D., Hanafy, M., Lotfy, Y., Deif, M., Elgohary, R. (2024). 'Real-time Driver Drowsiness Detection Using Deep Neural Networks', test, 1(2), pp. -. doi: 10.21608/iiis.2024.357785
Halim, D., Hanafy, M., Lotfy, Y., Deif, M., Elgohary, R. Real-time Driver Drowsiness Detection Using Deep Neural Networks. test, 2024; 1(2): -. doi: 10.21608/iiis.2024.357785

Real-time Driver Drowsiness Detection Using Deep Neural Networks

International Integrated Intelligent Systems
Volume 1, Issue 2, June 2024    PDF (629.14 K)
DOI: 10.21608/iiis.2024.357785
Authors
Daniel Halim1; Mariam Hanafy1; Youssef Lotfy1; Mohanad Deif2; Rania Elgohary3
1Faculty of Engineering Cairo University, Cairo, Egypt
2Department of Artificial intelligence , College of Information Technology, Misr University for Science & Technology (MUST), 6th of October City 12566 , Egypt
3Department of Artificial intelligence, College of Information Technology, Misr University for Science & Technology
Abstract
This paper presents a driver drowsiness  detection for accident prevention which is based on the curvature 
of the eye. Our attempt is to develop a deep learning model that  can use the input from a camera in real time by extracting the eyes  to detect the drowsiness of the drivers.This paper helps to resolve  the problem of drowsiness detection with an accuracy of 96% for  test and 99% for validation
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