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Using retina modelling to characterize blinking: comparison between EOG and video analysis

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Abstract

A large number of car crashes are caused by drowsiness every year. The analysis of eye blinks provides reliable information about drowsiness. This paper proposes to study the relation between electrooculogram (EOG) and video analysis for blink detection and characterization. An original method to detect and characterize blinks from a video analysis is presented here. The method uses different filters based on the human retina modelling. A illumination robust filter is first used to normalize illumination variations of the video input. Then, Outer and an Inner Plexiform Layer filters are used to extract energy signals from eye area. The eye detection is processed mixing gradient and projection methods which makes it able to detect even closed eyes. The illumination robust filter makes it possible to detect the eyes even in night conditions, without using external lighting. The video analysis extracts two signals from the eye area measuring the quantity of static edges and moving edges. Blinks are then detected and characterized from these two signals. A comparison between the features extracted from the EOG and the same features extracted from the video analysis is then performed on a database of 14 different people. This study shows that some blink features extracted from the video are highly correlated with their EOG equivalent: the duration, the duration at 50%, the frequency, the percentage of eye closure at 80% and the amplitude velocity ratio. The frame rate influence on the accuracy of the different features extracted is also studied and enlightens on the need of a high frame rate camera to detect and characterize accurately blinks from a video analysis.

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References

  1. Anscombe F.J.: Graphs in statistical analysis. Am. Stat. 27, 17–21 (1973)

    Google Scholar 

  2. Ben Khalifa K., Bdoui M.H., Dogui M., Alexandre F.: Alertness states classification by SOM and LVQ neural networks. Int. J. Inform. Technol. 4, 228–231 (2004)

    Google Scholar 

  3. Benoit A., Caplier A.: Motion Estimator Inspired from Biological Model for Head Motion Interpretation. WIAMIS, Montreux, Switzerland (2005)

    Google Scholar 

  4. Bergasa L.M., Nuevo J., Sotelo M.A., Barea R., Lopez M.E.: Real-time system for monitoring driver vigilance. IEEE Trans. Intell. Transp. Syst. 7(1), 63–77 (2006)

    Article  Google Scholar 

  5. Boverie, S., Giralt, A.: Driver vigilance diagnostic based on eyelid movement observation. In: Proceedings of the 17th IFAC World Congress, Seoul, South Korea (2008)

  6. Caffier P., Erdmann U., Ullsperger P.: Experimental evaluation of eyeblink parameters as a drowsiness measure. Eur. J. Appl. Physiol. 89, 319–325 (2003)

    Article  Google Scholar 

  7. Damousis, I., Cester, I., Nikolaou, S., Tzovaras, D.: Psychological indicators based sleep onset prediction for the avoidance of driving accidents. In: Proceedings of the 29th IEEE EMBS conference, Lyon, France (2007)

  8. Galley N., Schleicher R., Galley L.: Blink parameter as indicators of driver’s sleepiness—possibilities and limitations. Vis. Veh. 10, 189–196 (2004)

    Google Scholar 

  9. Georghiades A.S., Belhumeur P.N.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. PAMI 23, 643–660 (2001)

    Article  Google Scholar 

  10. Gillberg M., Kecklund G., Akerstedt T.: Sleepiness and performance of professional drivers in a truck simulator—comparisons between day and night driving. J. Sleep Res. 5, 12–15 (1996)

    Article  Google Scholar 

  11. Horng, W.-B., Chen, C.-Y., Chang, Y., Fan, C.-H.: Driver fatigue detection based on eye tracking and dynamk, template matching. In: Proceedings of the IEEE International Conference on Networking, Sensing and Control, Taipei Taiwan (2004)

  12. Jammes B., Sharabty H., Esteve D.: Automatic EOG analysis: a first step toward automatic drowsiness scoring during wake sleep transitions. Somnologie 12(3), 227–232 (2008)

    Article  Google Scholar 

  13. Ji Q., Yang X.: Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance. Real Time Imaging 8, 357–377 (2002)

    Article  MATH  Google Scholar 

  14. Jimenez P., Nuevo J., Bergasa L.M., Sotelo M.A.: Face tracking and pose estimation with automatic three-dimensional model construction. IET Comput. Vis. Special Iss. 3D Face Process. 3, 93–102 (2009)

    Google Scholar 

  15. Johns M.W., Tucker A., Chapman R., Crowley K., Michael N.: Monitoring eye and eyelid movements by infrared reflectance oculography to measure drowsiness in drivers. Somnologie 11, 234–242 (2007)

    Article  Google Scholar 

  16. Kalman R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)

    Article  Google Scholar 

  17. Knipling, R.: PERCLOS: a valid psychophysiological measure of alertness as assessed by psychomotor vigilance. Federal Highway Administration (FHWA) (1998)

  18. Kothari, R., Mitchell, J.L.: Detection of eye locations in unconstrained visual images. In: Proceedings of the IEEE ICIP conference, Lausanne, Switzerland (1996)

  19. Marcotte, T.D., Scot, J.C., Lazzaretto, D., Rosenthal, T.J.: Long-term stability of standard deviation of lateral position in neurocognitively normal and impaired individuals. Adv. Transp. Stud. Int. J. 57–65 (2004) (special issue)

  20. Muzet, A., Pbayle, T., Langrognet, J., Otmani, S.: AWAKE Pilot study no.2: Testing steering grip sensor measures, CEPA, IST-2000-28062 (2003)

  21. Picot, A., Caplier, A., Charbonnier, S.: Drowsiness detection based on visual signs: blinking analysis based on high frame rate video, Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, Austin (TX), USA (2010)

  22. Picot, A., Charbonnier, S., Caplier, A.: Monitoring drowsiness on-line using a single encephalographic channel. de Mello, C.A.B. (Ed.) Biomedical Engineering, pp. 145–164 (2010). ISBN 978-953-307-013-1

  23. Pilutti, T., Ulsoy, G.: On-line identification of driver state for lane-keeping tasks. In: Proceedings of the 14th American Control Conference, Seattle (WA), USA (1995)

  24. Renner, G., Mehring, S.: Lane departure and drowsines—two major accident causes—one safety system. Transport Research Laboratory (1997)

  25. Rosipal R., Peters B., Kecklund G., Akerstedt T., Gruber G., Woertz M., Anderer P., Dorffner G.: EEG-based drivers’ drowsiness monitoring using a hierarchical Gaussian mixture model. Hum. Comp. Interact. 16, 294–303 (2007)

    Google Scholar 

  26. Schleicher R., Galley N., Briest S., Galley L.: Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired?. Ergonomics 51(7), 982–1010 (2008)

    Article  Google Scholar 

  27. Shannon C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)

    MathSciNet  MATH  Google Scholar 

  28. Ueno, H., Kaneda, M., Tsukino, M.: Development of drowsiness detection system. In: Proceedings of the Vehicle Navigation and Information Systems Conference, Yokohama, Japan (1994)

  29. Viola P., Jones M.: Robust real-time object detection. Int. J. Comput. Vis. 57(2), 137–154 (2002)

    Article  Google Scholar 

  30. Viola, P., Jones, M.: Fast Multi-View Face detection. MERL, TR2003-96 (2003)

  31. Vu, N.-S., Caplier, A.: Illumination-robust face recognition using the retina modelling. Proceedings of the IEEE International Conference on Image Processing, Cairo, Egypt (2009)

  32. Zhou Z.-H., Geng X.: Projection functions for eye detection. Pattern Recogn. 37(5), 1049–1056 (2004)

    Article  MATH  Google Scholar 

  33. http://www.bioid.com/

  34. http://mplab.ucsd.edu/grants/project1/free-software/mptwebsite/API/index.html

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Correspondence to Antoine Picot.

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Picot, A., Charbonnier, S., Caplier, A. et al. Using retina modelling to characterize blinking: comparison between EOG and video analysis. Machine Vision and Applications 23, 1195–1208 (2012). https://doi.org/10.1007/s00138-011-0374-4

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  • DOI: https://doi.org/10.1007/s00138-011-0374-4

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