Abstract
Several studies have related the alertness of an individual to their eye-blinking patterns. Accurate and automatic quantification of eye-blinks can be of much use in monitoring people at jobs that require high degree of alertness, such as that of a driver of a vehicle. This paper presents a non-intrusive system based on facial biometrics techniques, to accurately detect and quantify eye-blinks. Given a video sequence from a standard camera, the proposed procedure can output blink frequencies and durations, as well as the PERCLOS metric, which is the percentage of the time the eyes are at least 80% closed. The proposed algorithm was tested on 360 videos of the AV@CAR database, which amount to approximately 95,000 frames of 20 different people. Validation of the results against manual annotations yielded very high accuracy in the estimation of blink frequency with encouraging results in the estimation of PERCLOS (average error of 0.39%) and blink duration (average error within 2 frames).
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References
Caffier, P.P., Erdmann, U., Ullsperger, P.: Experimental evaluation of eye-blink parameters as a drowsiness measure. European Journal of Applied Physiology 89(3-4), 319–325 (2003)
Caffier, P.P., Erdmann, U., Ullsperger, P.: The spontaneous eye-blink as sleepiness indicator in patients with obstructive sleep apnoea syndrome - a pilot study. Sleep Medicine 6(2), 155–162 (2005)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. computer vision and image understanding 61(1), 38–59 (1995)
Dinges, D.: PERCLOS: A valid psychophysiological measure of alertness as assesed by psychomotor vigilance indianapolis. Technical report, Federal Highway Administration, Office of Motor Carriers, Tech. Rep. MCRT-98-006 (1998)
D’Orazio, T., Leo, M., Guaragnella, C., Distante, A.: A visual approach for driver inattention detection. Pattern Recognition 40(8), 2341–2355 (2007)
MartÃnez, A., Benavente, R.: The AR face database. Technical report, Computer Vision Center, Barcelona, Spain (1998)
Morris, T.L., Miller, J.C.: Electrooculographic and performance indices of fatigue during simulated. Biological psychology 42(3), 343–360 (1996)
Ortega, A., Sukno, F.M., Lleida, E., Frangi, A.F., Miguel, A., Buera, L., Zacur, E.: AV@CAR: A spanish multichannel multimodal corpus for in-vehicle automatic audio-visual speech recognition. In: Proc. 4th Int. Conf. on Language Resources and Evaluation, Lisbon, Portugal, vol. 3, pp. 763–767 (2004), http://www.cilab.upf.edu/ac
Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: ICCV 1998: Proceedings of the International Conference on Computer Vision, Washington, DC, USA, pp. 555–562 (1998)
Pavani, S.-K., Delgado-Gomez, D., Frangi, A.F.: Haar-like features with optimally weighted rectangles for rapid object detection. Pattern Recognition (in press, 2009)
Selinger, A., Socolinsky, D.: Appearance-based facial recognition using visible and thermal imagery: a comparative study. Technical report, Equinox Corporation (2002), http://www.equinoxsensors.com
Sirevaag, E.J., Stern, J.A.: Ocular Measures of Fatigue and Cognitive Factors. In: Engineering Psychophysiology: Issues and Applications, L. Erlbaum Associates Press, Mahwah (1998)
Smith, P., Shah, M., da Vitoria Lobo, N.: Determining driver visual attention with one camera. IEEE Trans. Intell. Transport. Syst. 4(4), 205–218 (2003)
Stern, J.: Eye activity measures of fatigue, and napping as a countermeasure. Technical report, U.S. Department of Transportation. Technical Report FHWA-MC-99-028 (1999)
Stern, J.A., Beideman, L., Chen, S.C.: Effect of alcohol on visual search and motor performance during complex task performance. In: Adverse effects of environmental chemicals and psychotropic drugs: neurophysiological and behavioral tests, vol. 2, pp. 53–68. Elsevier, Amsterdam (1976)
Sukno, F.M., Ordas, S., Butakoff, C., Cruz, S., Frangi, A.F.: Active shape models with invariant optimal features: Application to facial analysis. IEEE Trans. Pattern Anal. Mach. Intell. 29(7), 1105–1117 (2007)
Wierwille, W.W., Ellsworth, L.A., Wreggit, S.S., Fairbanks, R.J., Kirn, C.L.: Research on vehicle based driver status/performance monitoring: development, validation, and refinement of algorithms for detection of driver drowsiness. Technical report, National Highway Traffic Safety Administration Final Report: DOT HS 808 247 (1994)
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Sukno, F.M., Pavani, SK., Butakoff, C., Frangi, A.F. (2009). Automatic Assessment of Eye Blinking Patterns through Statistical Shape Models. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_4
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DOI: https://doi.org/10.1007/978-3-642-04667-4_4
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