Deep Learning-Based Driver Drowsiness Detection Using Facial Expression Analysis | ||||
| Delta University Scientific Journal | ||||
| Volume 7, Issue 2, September 2024, Page 164-176 PDF (902.4 K) | ||||
| Document Type: Original research papers | ||||
| DOI: 10.21608/dusj.2024.248347.1032 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Tarek Mohammed Hassan   1; Walaa Yahia Ibrahim2; Hesham A. Ali3; Hanaa Salem Marie4
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| 1Communications and Computers Dept. Faculty of Engineering, Delta university for Science and Technology | ||||
| 2Student | ||||
| 3Faculty Artificial Intelligence, Delta University for Science and Technology, Gamasa 35712, Egypt | ||||
| 4Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa 35712, Egypt | ||||
| Abstract | ||||
| Driver Drowsiness Detection is technology that prevent the driver from accidents caused by driver fatigue that lead to fall asleep while driving. There are many factors that cause road accidents but driver drowsiness is the most contributor one to make a wide and deadly accidents. There are many features that make sure that the driver falls asleep like mouth opening, close eyes, yawn and head tilt. The objective of this paper is to introduce a Driver Drowsiness Detection alarming system based on a CNN (Convolutional Neural Network) for accurate detection and OpenCV to use camera for video and capture image. In this paper, an algorithm is proposed to detect the Driver Drowsiness through eyes, which determine if the eye is closed or open. When the eyes are closed for period of time, the alarm turned on. The experimental results show that the system achieves high accuracy, reducing the overall number of accidents on the streets. For Real-time video, the proposed method has achieved 97% of accuracy. | ||||
| Keywords | ||||
| Facial Extraction; Machine Learning; Eye extraction; Face Detection; CNN | ||||
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