Original Article

Determination the Levels of Subjective and Observer Rating of Drowsiness and Their Associations with Facial Dynamic Changes


Background: We determined the levels of subjective and observer drowsiness and facial dynamics changes.

Methods: This experimental study was done in the virtual reality laboratory of Khaje-Nasir Toosi University of Technology in 2015. Facial dynamics changes like changes in eyes, mouth and eyebrows were surveyed on twenty-five drivers in 2015byKSS (Karolinska Sleepiness Scale) and ORD (Observer Rating of Drowsiness). ANOVA Repeated Measure and MANOVA Repeated Measure tests were used for data analysis. Also, neural network and Viola-Jones were used to detect facial characteristics. PERCLOS (Percentage of Eye Closure), blink frequency and blink duration were inspected for eyes parameters. The size of open mouth during drowsiness was inspected for mouth parameter. During the inspection of eyebrow, the number 50 denoted eyebrow in normal position. For eyebrows above the normal position, a range of 50 to 55 was specified; in addition, 45-50 was found as the specified range for eyebrows under normal position.

Results: Descriptive statistics of the dynamic changes in mouth and eyes illustrated that during the driving process, the level of sleepiness increased as well as changes of eyes and mouth. However, statistical findings during car driving revealed that dynamic changes in eyebrows had clear expression with a constant trend. Similar studies on data obtained from KSS and ORD showed that both of these parameters simultaneously increased as well as the level of drowsiness. In addition, a significant relationship existed between facial expression and drowsiness.

Conclusion: This research would be an effective and efficient tool for timely alarming and detecting the drowsiness quickly and precisely.



WHO (2009). Global Status Report on Road Safety Geneva, Switzerland.


Rau P (2005).Drowsy Driver Detection and Warning System for Commercial Vehicle Drivers, Field Operational Test Design, Analysis, and Progress. National Highway Traffic Safety Administration Washington, DC, USA. Paper Number 05-0192.

Bergasa LM, Nuevo Ju, Sotelo MA, Barea R, Lopez E (2008).Visual monitoring of driver in attention. In: Studies in Computational Intelligence. Eds. Springer, pp.19-37.

Knipling RR, Wang, JS (1994).Crashes and fatalities related to driver drowsi-ness/fatigue. US Department of Transportation, National Highway Traffic Safety Administration, Office of Crash Avoidance Research, Research & Development. www.ntl.bts.gov/lib/jpodocs/repts_te/1004.pdf .

Anonymous (2000)."Tomorrow’s Roads": Safer for Everyone, Department of the Environment, Transport and the Regions: London. www.ocs.polito.it/biblioteca/mobilita/TomorrowRoads1.pdf.

Zhang C, Lin X, Lu R, Ho PH, Shen X (2008).An efficient message authentication scheme for vehicular communications. IEEE Tran Veh Technol, 57) 6 :(3357-68.

Manvi SS, Kakkasageri MS, Pitt J (2009). Multiagent based information dissemina-tion in vehicular ad hoc networks. Mobile Inform Sys, 5) 4 :(363-89.

Liu CC, Hosking SG, Lenné MG (2009). Predicting driver drowsiness using vehicle measures:Recent insights and future chal-lenges. J Safety Res, 40 (4): 239-45.

Barbato G, Ficca G, Beatrice M, Casiello M, Muscettola G, Rinaldi F (1995). Effects of sleep deprivation on spontaneous eye blink rate and alpha EEG power. Biol Psy-chiatry, 38 (5): 340-1.

Conner J, Norton R, Ameratunga S, Robin-son E, Civil I, Dunn R, Bailey j, Jackson R (2002). Driver sleepiness and risk of serious injury to car occupants: population based case control study. BMJ, 324 (7346): p.1125.

Dinges D, Maislin G, Brewster R M, Krueger G P, Carroll RJ (2005). Pilot test fatigue management technologies. Transp Res Board , 1922:175-182.

Ji Q, Zhu Z, Lan P. (2004). Real-time nonin-trusive monitoring and prediction of driver fatigue. IEEE Trans veh technol, 53(4): 1052-68.

Bergasa LM, Nuevo J, Sotelo MA, Barea R, Lopez ME (2006). Real-Time system for monitoring driver vigilance. IEEE T Intell Transp, 7 (1): 63-77.

Johns MW (2003).The Amplitude velocity Ratio of Blinks: A new method for moni-toring drowsiness. Paper presented at the 7th Annual Conference, Associated Professional Sleep Societies.

Johns MW, Tucker A, Chapman R, Crowley K, Michael N (2007). Monitoring eye and eyelid movement by infrared reflectance oculography to measure drowsiness in driver. Somnologie, 11 (4): 234-42.

Dai Y, Nakano Y (1996). Face-Texture Model Based on SGLD and Its Application in Face Detection in a Color Scene. Pattern Recogn, 29 (6):1007-17.

Ying Y, Jing S, Wei Z (2007). The Monitoring Method of Driver’s Fatigue Based on Neural Network. Proc. International Conf. on Mechatronics and Automation, China.pp.3555-9.

Sahayadhas A, Sundaraj K, Murugappan M (2012). Detecting driver drowsiness based on sensors: a review. Sensors,12(12): 16937-16953.

Saradadevi M, Bajaj P (2008).Driver Fatigue Detection Using Mouth and Yawning Analysis. IJCSNS, 8(6):183-8.

Lopar M, and Ribarić S (2013). An Overview and Evaluation of Various Face and Eyes Detection Algorithms for Driver Fatigue Monitoring Systems. Proceedings of the Croatian Computer Vision Workshop, Year 1. Zagreb, Coroatia, pp.15-18. https://arxiv.org/ftp/arxiv/papers/1310/1310.0317.pdf.

Ji Q, Yang X (2002).Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance. Real-Time Imaging, 8 (5): 357-77.

Yang G, Huang T S (1994). Human Face Detection in Complex Background. Pattern Recogn, 27 (1): 53-63.

Otmani S, Pebayle T, Roge J, Muzet A (2005).Effect of driving duration and par-tial sleep deprivation on subsequent alert-ness and performance of car drivers. Phy-siol Behav, 84 (5): 715–24.

McDonald AD, Schwarz C, Lee JD, Brown TL (2012). Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle. Proc Hum Factors Ergon Soc Annu Meet. 56(1) 2201- 2205.

Drivingsimulator.ir (Internet). Tehran: driving Simulator of Khaje Nasir, Inc; ( Updated 2013 may 16) http://www.drivingsimulator.ir/services-content/news.html

Craig A, Tran Y, Wijesuriya N, Boord PA (2006). A Controlled Investigation in to the Psychological Determinants of Fati-gue. Biol Psychol, 72 (1): 78-87.

Viola P(2004).Robust real-time face detec-tion. Int Jcomput vision, 57(2):137–54.

Azim T, Jaffar MA , Mirza AM (2009). Au-tomatic Fatigue Detection of Drivers through Pupil Detection and Yawning Analysis. Innovative Computing, Information and Control (ICICIC). Fourth International Conference on Source .pp. 441-445

Belz Steven M, Robinson Gary S, Casali John G (2004). Temporal Separation and Self-Rating of Alertness as Indicators of Driver Fatigue in Commercial Motor Vehicle Operators. Human Factors, 46(1):154-69.

Timm F, Barth E (2011).Accurate eye Centre localization by means of gradients. Proc of the Sixth International Conference on Computer Vision Theory and Applica-tions, Vilamoura, Algarve, Portugal. www.inb.uniluebeck.de/fileadmin/files/PUBPDFS/TiBa11b.pdf .

Kumar N, Barwar NC (2014).Analysis of Real Time Driver Fatigue Detection Based on Eye and Yawning. IJCSNS,5 (6): 7821-7826.

Ingre M, ÅKerstedt T, Peters B, Anund, A, Kecklund G (2006).Subjective sleepiness simulated driving performance and blink duration: Examining individual differenc-es. J. Sleep Res. 15(1): 47–53.

Xuanpeng L, Seignez E, Loonis P (2013).Driver drowsiness estimation by fusion of lane and eye features using a multilevel evidence theory. IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems. DOI: 10.1109/CYBER.2013.6705481.

Karchani M, Mazloumi A, Saraji GN, Gharagozlou F, Nahvi A, Sadeghniiat Haghighi Kh, Makki Abadi B, Rahimi Foroshani A (2015). Presenting a model for dynamic facial expression changes in detecting drivers’ drowsiness. Electron Physician, 7(2): 1073-77.

Reddy K, Sikandar A, Savant P, Choudhary A (2014).Driver Drowsiness Monitoring Based On Eye Map and Mouth Contour. IJSTR, 3(5): 147- 156.

Vural E, Cetin M, Ercil A, Littlewort G, Bar-tlett M, Movellan J (2007).Drowsy Driver Detection through Facial Movement Analysis. Human-Computer Interaction, IEEE International Workshop, HCI, Rio de Ja-neiro, Brazil. Volume 4796 of the series Lecture Notes in Computer Science. pp 6-18.

Karchani M, Mazloumi A, NaslSaraji G, Ak-barzadeh A, Niknezhad A, Ebrahimi MH, Raei M, Khandan M (2015). Association of Subjective and Interpretive Drowsiness with Facial Dynamic Changes in Simulator Driving. J Res Health Sci, 15(4): 250-55.

IssueVol 46 No 1 (2017) QRcode
SectionOriginal Article(s)
Driver drowsiness Facial dynamic changes ORD KSS

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How to Cite
POURSADEGHIYAN M, MAZLOUMI A, NASL SARAJI G, NIKNEZHAD A, AKBARZADEH A, EBRAHIMI MH. Determination the Levels of Subjective and Observer Rating of Drowsiness and Their Associations with Facial Dynamic Changes. Iran J Public Health. 2017;46(1):93-102.