Statistical Relationship among Driver’s Drowsiness, Eye State and Head Posture

Lam Thanh Hien, Thanh-Lam Nguyen, Do Nan Toan


Many serious accidents in road traffic are resulted from driver’s drowsiness, leading to special efforts in improving traffic safety by searching for optimal models to accurately detect and alert driver’s drowsiness. Thus, numerous scholars worldwide have paid special interest in proposing a great number of detection methods, among which visual feature-based approaches, such as eye state, head movement, yawning, facial expressions, etc., have been most preferred as they are non-intrusive and effectively detect drowsiness. However, the current literature fails to show the statistical relationships among the driver’s drowsiness, eye state and head posture. Thus, the statistical linear regression and binary logistic regression models found in this paper fill the existing gap; especially, the eye state should be determined by simultaneously monitoring the eye states of both eyes and it has greater impact on the detection ability than that of head posture. More importantly, the interactive combination of eye state and head posture provides better detection ability. Our proposed logistic regression model can correctly detect 99.1% of the total investigated observations in a practical experiment study.


Driver’s drowsiness, drowsiness detection, eye state, head posture, statistical relationships, Linear regression, Logistic regression

Full Text:



L.T. Hien, T.V. Lang, H.M. Toan and D.N. Toan, Modeling the human face and its application for detection of driver drowsiness, International Journal of Computer Science and Telecommunications, 3 (2012), 56-59.

L.T. Hien and D.N. Toan, An algorithm to detect driver's drowsiness based on nodding behaviour, International Journal of Soft Computing, Mathematics and Control, 5 (2016), 1-8, DOI: 10.14810/ijscmc.2016.5101.

M. Eriksson and N.P. Papanikotopoulos, Eye-tracking for detection of driver fatigue, In Proceedings of the IEEE conference on intelligent transportation systems, (1997), 314–319.

S. Kawato and J. Ohya, Real-time detection of nodding and headshaking by directly detecting and tracking the between-eyes, In Proceedings of 4th international IEEE conference on automatic face and gesture recognition, (2002), 40–45.

N. Parmar, Drowsy driver detection system, Engineering Design Project, Thesis, (2002), Ryerson University.

A. Kircher, M. Uddman and J. Sandin, Vehicle control and drowsiness, Swedish National Road and Transport Research Institute, (2002).

W. Rongben, G. Lie, T. Bingliang and J. Lisheng, Monitoring mouth movement for driver fatigue or distraction with one camera. In Proceedings of the IEEE international conference on intelligent transportation systems, (2004), 314–319.

K. Torkkola, N. Massey and C. Wood, Driver inattention detection through intelligent analysis of readily available sensors, Proceedings of the IEEE International Conference on Intelligent Transportation Systems, (2004), 326–331.

L.M. Bergasa, J. Nuevo, M.A. Sotelo, R. Barea and M.E. Lopez, Real-time system for monitoring driver vigilance, IEEE Transactions on Intelligent Transportation Systems, 7 (2002), 63–77.

E. Murphy-Chutorian, A. Doshi and M.M. Trivedi, Head pose estimation for driver assistance systems: A robust algorithm and experimental evaluation, In Proceedings of 10th international IEEE conference on intelligent transportation systems, (2007), 709–714.

T.D. Orazio, M. Leo, C. Guaragnella and A. Distante, A visual approach for driver inattention detection, Pattern Recognition, 40 (2007), 2341–2355.

E. Vural, M. Çetin, A. Ercil, G. Littlewort, M.S. Bartlett and J.R. Movellan, Drowsy driver detection through facial movement analysis, In Proceedings of the IEEE international conference on human–computer interaction, (2007), 6–18.

J. Wu and M.M. Trivedi, Simultaneous eye tracking and blink detection with interactive particle filters, EURASIP Journal on Advances in Signal Processing, (2008), 823695-1–823695-20.

J.D. Wu and T.R. Chen, Development of a drowsiness warning system based on the fuzzy logic images analysis, Expert Systems with Applications, 34 (2008), 1556–1561.

J. Adachi, T. Hayashi, K. Ogawa, T. Suzuki, H. Ishiguro, T. Nishina, K. Ohue, S. Uozumi, S. Kojima and S. Nakanishi, Development of a sensor detecting eyelid positions, In 15th World congress on ITS, Jacob K. Javits Convention Center, New York City, (2008).

I.G. Damousis and D. Tzovaras, Fuzzy fusion of eyelid activity indicators for hypovigilance-related accident prediction, IEEE Transactions on Intelligent Transportation Systems, 9 (2008), 491–500.

J.W. Li, Eye blink detection based on multiple Gabor response waves, In Proceedings of the international conference on machine learning and cybernetics, (2008), 2852–2856.

M. Saradadevi and P. Bajaj, Driver fatigue detection using mouth and yawning analysis, International Journal of Computer Science and Network Security, 8 (2008), 183–188.

L. Wang, X. Ding, C. Fang and C. Liu, Eye blink detection based on eye contour extraction, Proceedings of SPIE Image Processing: Algorithms and Systems VII, 7245.72450R (2009), 1-7.

J.H. Yang, Z.H. Mao, L. Tijerina, T. Pilutti, J. Coughlin and E. Feron, Detection of driver fatigue caused by sleep deprivation, IEEE Transactions on Systems, Man, Cybernetics. A, Systems Humans, 39 (2009), 694–705.

L. Yunq, Y. Meiling, S. Xiaobing, L. Xiuxia, O. Jiangfan, Recognition of eye states in real time video. In Proceedings of the International Conference on Computer, Engineering and Technology, (2009), 554–559.

C.C. Liu, S.G. Hosking and M.G. Lenn, Predicting driver drowsiness using vehicle measures: Recent insights and future challenges, Journal of Safety Research, 40 (2009), 239–245.

B. Bhowmick and K.S.C. Kumar, Detection and classification of eye state in IR camera for driver drowsiness identification, In Proceedings of the IEEE International Conference on Signal and Image Processing Applications, (2009), 340–345.

I.F. Ince and T. Yang, A new low-cost eye tracking and blink detection approach: Extracting eye features with blob extraction, Emerging Intelligent Computing Technology and Applications, LNCS, 5754 (2009), 526–533.

J. Jimenez-Pinto and M. Torres-Torriti, Driver alert state and fatigue detection by salient points analysis. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, (2009), 455–461.

Y. Noguchi, K. Shimada, M. Ohsuga, Y. Kamakura and Y. Inoue, The assessment of driver’s arousal states from the classification of eye-blink patterns. In Proceedings of the International Conference on Engineering Psychology and Cognitive Ergonomics, (2009), 414–423.

F.M. Sukno, S.K. Pavani, C. Butakoffand and A.F. Frangi, Automatic assessment of eye blinking patterns through statistical shape models, In ICVS 2009. LNCS, 5815 (2009), 33–42.

H. Shuyan and Z. Gangtie, Driver drowsiness detection with eyelid related parameters by support vector machine, Expert Systems with Applications, 36 (2009), 7651–7658.

Y. Tran, A. Craig, N. Wijesuriya and H. Nguyen, Improving classification rates for use in fatigue countermeasure devices using brain activity, In Proceedings of IEEE international conference on engineering in medicine and biology society (EMBC), (2010), 4460–4463.

A. Tsuchida, M.S. Bhuiyan and K. Oguri, Estimation of drivers’ drowsiness level using a neural network based ‘error correcting output coding’ method, In Proceedings of the IEEE International Conference on Intelligent Transportation Systems, (2010), 1887–1892.

T. Ersal, H.J.A. Fuller, O. Tsimhoni, J.L. Stein amd H.K. Fathy, Model-based analysis and classification of driver distraction under secondary tasks, IEEE Transactions on Intelligent Transportation Systems, 11 (2010), 692–701.

M.J. Flores and J.M. Armingol, Real-time warning system for driver drowsiness detection using visual information, Journal of Intelligent and Robotic Systems, 59 (2010), 103–125.

J. Jo, S.J. Lee, Y.J. Lee, H.G. Jung, K.R. Park and J. Kim, An edge-based method to classify open and closed eyes for monitoring driver’s drowsiness. In Proceedings of the International Conference of Electronics, Information and Communication (ICEIC), (2010), 510–513.

J. Jo, S.J. Lee, H.G. Jung, K.R. Park and J. Kim, A vision-based method for detecting driver’s drowsiness and distraction in driver monitoring system, Optical Engineering, 50 (2011), 127202-1–127202-24.

Y. Dong, Z. Hu, K. Uchimura and N. Murayama, Driver inattention monitoring system for intelligent vehicles: A review, IEEE Transactions on Intelligent Transportation Systems, 12 (2011), 596–614.

A. Panning, A. Al-Hamadi and B. Michaelis, A color based approach for eye blink detection in image sequences. In Proceedings of the IEEE International Conference on Signal and Image Processing Applications (ICSIPA), (2011), 40–45.

M. Patel, S.L.L. Lal, D. Kavanagh and P. Rossiter, Applying neural network analysis on heart rate variability data to assess driver fatigue, Expert Systems with Applications, 38 (2011), 7235–7242.

Y. Kurylyak, F. Lamonaca and G. Mirabelli, Detection of the eye blinks for human’s fatigue monitoring, In Proceedings of the IEEE International Symposium on Medical Measurements and Applications (MeMeA), (2012), 1–4.

K. Minkov, S. Zafeiriou and M. Pantic, A comparison of different features for automatic eye blinking detection with an application to analysis of deceptive behavior, In Proceedings of the International Symposium on Communications Control and Signal Processing (ISCCSP), (2012), 1-4.

M. Uliyar and S. Ukil, A fast blink detector using canonical correlation analysis. In Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), (2012), 33–34.

J. Jo, S.J. Lee, K.R. Park, I.J. Kim, and J. Kim, Detecting driver drowsiness using feature-level fusion and user-specific classification, Expert System with Applications, 41 (2014), 1139-1152.

W.W. Wierwille, L.A. Ellsworth, S.S. Wreggit, R.J. Fairbanks and C.L. Kim, Research on vehicle-based driver status/performance monitoring: development, validation, and refinement of algorithms for detection of driver drowsiness, National Highway Traffic Safety Administration, (1994), Final Report: DOT HS 808 247.

B. Benfold and I. Reid, Guiding visual surveillance by tracking human attention, Proceedings of 20th British Machine Vision Conference (BMVC2009), (2009), 14.1-14.11.

K. Smith, S.O. Ba, J.M. Odobez and D. Gatica-Perez, Tracking the visual focus of attention for varying number of wandering people, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 (2008), 1212-1229.

I. Chamveha, Y. Sugano, D. Sugimura, T. Siriteerakul, T. Ohabe, Y. Sta and A. Sugimoto, Head direction estimation from low resolution images with scene adaptation, Computer Vision Image Understanding, 117 (2013), 1502-1511.

J.V.D. Berg, Sleepiness and head movements, Industrial Health, 44 (2006), 564-576.

Q. Ji and X. Yang, Real-time eye, gaze, and face pose tracking for monitoring driver vigilance, Real-Time Imaging, 8 (2002), 357–377, doi:10.1006/rtim.2002.0279.

T. Kito, M. Haraguchi, T. Funatsu, M. Sato and M. Kondo, Measurements of gaze movements while driving, Perceptual and Motor Skills, 68 (1989), 19–25.

J.F. Hair, W.C. Black, B.J. Babin and R.E. Anderson, Multivariate Data Analysis, 7th Edition, Prentice Hall, Upper Saddle River, New Jersey, (2010).


eISSN 0975-5748; pISSN 0974-875X