Drowsiness Detection System Based on Machine Learning Using Eye State

Abstract

Drowsiness is one of the major causes of driver-induced traffic accidents. The interactive systems developed to reduce road accidents by alerting drivers is called as Advanced Driver Assistance Systems (ADAS). The most important ADAS are Lane Departure Warning System, Front Collision Warning System and Driver Drowsiness Systems. In this study, an ADAS system based on eye state detection is presented to detect driver drowsiness. First, Viola-Jones algorithm approach is used to detect the face and eye areas in the proposed method. The detected eye region is classified as closed or open by making use of a machine learning method. Finally, the eye conditions are analyzed at time domain with PERcentage of eyelid CLOsure (PERCLOS) metric and drowsiness conditions are determined by Support Vector Machine (SVM), kNN and decision tree classifiers. The proposed methods tested on 7 real people and drowsiness states are detected at 99.77%, 94.35%, and 96.62% accuracy, respectively.

Keywords

• Driver Drowsiness
• ADAS
• PERCLOS

References

1. V. Vibin, S. Amritha, K. Sreeram and K. P. Remya. “Ear based driver drowsiness detection system”, IOSR Journal of Engineering, 2018.
2. J. A. Ojo, L. T. Omilude, and I. A. Adeyemo. “Fatigue detection in drivers using eye-blink and yawning analysis”, International Journal of Computer Trends and Technology, vol. 50, no 2. 2017.
3. S. Sooksatra, T. Kondo, P. Bunnun and A. Yoshitaka, 2018, “A drowsiness detection method based on displacement and gradient vectors”, Songklanakarin J. Sci. Tech. vol. 40 no. 3, 2018, pp. 602-608.
4. C. In-Ho and K. Yong-Guk, “Head pose and gaze direction tracking for detecting a drowsy driver”, Appl. Math. Inf. Sci. vol. 9, No. 2L, 2015, pp. 505-512.
5. M. J. Flores and J. M. Armingol, “Real-time warning for driver drowsiness detection using visual information”, Journal of Intelligent and Robotic Systems vol. 59, no. 2, 2010, pp:103-125.
6. O. Gietelink and J. Ploeg, “Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations”, Vehicle System Dynamics, Vol. 44, no. 7, 2006, pp. 569–590.
7. S. S. Nagargoje and D. S. Shilvant, “Drowsiness detection system for car assisted driver using image processing”, International Journal of Electrical and Electronics Research, Vol. 3, no. 4, 2015, pp: 175-179.
8. T. K. Dange and T. S. Yengatiwar, “A review method on drowsiness detection system”, International Journal of Engineering Research & Technology, vol. 2, issue 1, 2013.

9. I. G. Daza, N. Hernandez, L. M. Bergasa, I. Parra, I., J. J. Yebes, M. Gavilan, R. Quintero, D. F. Llorca, M. A. Sotelo, “Drowsiness monitoring based on driver and driving data fusion”, 14th International IEEE Conference on Intelligent Transportation Systems, Washington, DC, USA, 2011.
10. D. Sanka, F. Dileepa, J. Sadari, W. Sandareka, and D. Chathura, Efficient PERCLOS and gaze measurement methodologies to estimate driver attention in real time, 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation, 2014.
11. A. D. Panicker and M. S. Nair, “Open-eye detection using iris–sclera pattern analysis for driver drowsiness detection”, Sadhana, vol. 42, no. 11, 2017, pp. 1835–1849.
12. T. P. Nguyen, M. T. Chew and S. N. Demidenko, “Eye tracking system to detect driver drowsiness”, 6th International Conference on Automation, Robotics and Applications, Queenstown, New Zealand, 2015.
13. B. N. Manu, “Facial features monitoring for real time drowsiness detection”, 12th International Conference on Innovations in Information Technology (IIT), 2016.
14. S. Junaedi, H. Akbar,H. “Driver drowsiness detection based on face feature and PERCLOS”, International Conference on Computation in Science and Engineering, 2018.
15. B. S. M. Caio, C. M. Márcio, M. M. de S. João, M. do N. Lucas, B. M. July, D. L. Isis and L. D. Enrique, “Real-time SVM classification for drowsiness detection using eye aspect ratio”, Probabilistic Safety Assessment and Management PSAM 14, 2018.
16. V. Sofia, R. Brian, R. Anca, van der K. Esther, “Confusion matrix-based feature selection, Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference”, Cincinnati, Ohio, USA, 2011.