Improved Driver Drowsiness Monitoring System using Real-time Eye Blinking Method

Aree A. Mohammed1 , Mohammed Q. Kheder1 and Azhee W. Muhammed3

1 Faculty of Science and Science Educations, Computer Department, University of Sulaimani, Kurdistan Region, Iraq.
3 School of Basic Education, Computer Department, University of Sulaimani, Kurdistan Region, Iraq.

Drivers with a diminished vigilance level suffer from a marked decline in their
perception; recognition and vehicle control abilities and therefore pose a serious danger
to their own lives and the lives of the other people. According to the National Highway
Traffic Safety Administration (NHTSA), about 100,000 crashes are the direct result of
driver drowsiness each year. This is the reason why more and more researches are
made to build automatic detectors of this dangerous state. In this paper, an efficient
drowsiness detection system based on eye state (close and open) is developed.The
camera should be fixed in front of the drivers to capture real time frames. After that,
the results from the camera (new frames) are subject to the some vision-based
algorithm to detect the eyes. Finally, the eye blink detection is applied to determine the
state of eyes. There are two states: open state and close state. Based on eye state the
warming alarm or telephone calling should be done to the drivers for preventing
undesired accident. The proposed system will be tested with different light conditions,
namely, day and night vision to show the performance in term of efficiency and
accuracy. The results explain that the proposed system which is based on Android
platform has a high accuracy rate (%97.2) for drowsiness detection especially during
the night vision.

Key Wordsdrowsiness detection, eye blinking, eye tracking, detection accuracy


 [1] Srijayathi K., and Vedachary M., "Implementation of the Driver Drowsiness Detection System", IJSETR. 2(9),
pp. 1751-1754, (2013).
[2] Dr. Suryaprasad J, Sandesh D, Saraswathi V, Swathi D, Manjunath S, "Real Time Drosy Driver Detection using
HaarCascade Samples", Computer Science and IT Proceeding Conference, Vol. 13, pp. 45-54, (2013).
 [3] Caffier P., Erdmann U., and Ullsperger P., "Experimental evaluation of eyeblink parameters as a drowsiness
measure", European Journal of Applied Physiology, 89, pp. 319–325, (2003).
[4] Aree A., Shereen A., "Efficient Eye Blink Detection Method for Disabled-Helping Domain", IJACSA, 5(5), pp.
202-206, (2014).
 [5] Abhi R., Seema V., and Chenta B., "Accident Prevention Using Eye Blinking and Head Movement", IJCA, 1(4),
pp. 18-22, (2012).
 [6] Qiang J., Zhiwei Z., and Peilin L., "Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue", IEEE
Transaction on Vehicular Technology, 53( 4), pp. 1052-1068, (2004).

[7] Mandeep S., Gagandeep K., "Drowsy Detection On Eye Blink Duration Using Algorithm", IJETAE, 2(4), pp.
363-365, (2012).
[8] Chuang-Wen Y., et al, "CarSafe App: Alerting Drowsy and Distracted Drivers using Dual Cameras on
Smartphones", MobiSys. ACM Conference, pp. 461-462, (2013).
[9] Robert L., "OpenCV 2 Computer Vision Application Programming Cookbook", Packt Publisher, ISBN 978-1-
849513-24-1, UK, (2011).
[10] Hrishikesh B., "Drowsy Detection and Alarming System", Proceedings of the World Congress on Engineering
and Computer Science, USA, pp. 267-270, (2007).
 [11] Loris N., et al, "Effective and precise face detection based on color and depth data", Journal of
Applied Computing and Informatics, December 9-14, Vol. 10, Issues 1-2, pp. 1-13, (2014).