Driver Drowsiness Detection System By Measuring EAR and MAR

Zoya Fatema Khan; Alfiya Sheikh; Vanshika Makode; Bhavika Makode; Dr Vanita Buradkar

Driver Drowsiness Detection System By Measuring EAR and MAR

June 2025

Vol-11, Issue-3

Paper ID: 26920

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Engineering

Keywords

Driver Drowsiness Detection Python OpenCV Dlib Computer Vision Eye Aspect Ratio Real-Time Alert System Road Safety Facial Landmark Detection Pygame Alarm

Abstract

- Driver drowsiness is one of the major causes of road accidents, leading to severe injuries and fatalities worldwide. In this paper, we propose a real-time Driver Drowsiness Detection System using Python, OpenCV, and Dlib to monitor driver alertness and provide timely alerts to prevent accidents. The system uses a webcam to continuously capture facial images and applies computer vision techniques to detect facial landmarks, particularly focusing on the eyes. By calculating the Eye Aspect Ratio (EAR) over a sequence of frames, the system determines if the driver’s eyes remain closed for an extended period, indicating drowsiness. Once drowsiness is detected, an alarm sound is played using the Pygame library to immediately alert the driver. The implementation aims to improve road safety by providing a low-cost, easily deployable solution using widely available hardware and open-source tools. This system can be integrated into various vehicles, offering an additional layer of safety for long-distance drivers, truck drivers, and night-time travellers.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Zoya Fatema Khan	Rajiv Gandhi College Of Engineering Research & Technology
2	Alfiya Sheikh	Rajiv Gandhi College Of Engineering Research & Technology
3	Vanshika Makode	Rajiv Gandhi College Of Engineering Research & Technology
4	Bhavika Makode	Rajiv Gandhi College Of Engineering Research & Technology
5	Dr Vanita Buradkar	Rajiv Gandhi College Of Engineering Research & Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

Khan, Zoya Fatema, Sheikh, Alfiya, Makode, Vanshika, Makode, Bhavika, & Buradkar, Dr Vanita (2025). Driver Drowsiness Detection System By Measuring EAR and MAR. International Journal of Advance Research and Innovative Ideas In Education, 11(3), 3708-3713.

MLA Style

Khan, Zoya Fatema, et al. "Driver Drowsiness Detection System By Measuring EAR and MAR." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 3, 2025, pp. 3708-3713.

IEEE Style

Zoya Fatema Khan, Alfiya Sheikh, Vanshika Makode, Bhavika Makode, and Dr Vanita Buradkar, "Driver Drowsiness Detection System By Measuring EAR and MAR," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 3, pp. 3708-3713, 2025.

Vancouver Style

Khan Zoya Fatema, Sheikh Alfiya, Makode Vanshika, Makode Bhavika, Buradkar Dr Vanita. Driver Drowsiness Detection System By Measuring EAR and MAR. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(3):3708-3713.

Harvard Style

Khan, Zoya Fatema, Sheikh, Alfiya, Makode, Vanshika, Makode, Bhavika, & Buradkar, Dr Vanita (2025) 'Driver Drowsiness Detection System By Measuring EAR and MAR', International Journal of Advance Research and Innovative Ideas In Education, 11(3), pp. 3708-3713.

Chicago Style

Khan, Zoya Fatema, et al. "Driver Drowsiness Detection System By Measuring EAR and MAR." International Journal of Advance Research and Innovative Ideas In Education 11, no. 3 (2025): 3708-3713.

Turabian Style

Khan, Zoya Fatema, et al. "Driver Drowsiness Detection System By Measuring EAR and MAR." International Journal of Advance Research and Innovative Ideas In Education 11, no. 3 (2025): 3708-3713.