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Eye Status Based on Eyelid Detection: A Driver Assistance System

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8671))

Abstract

Fatigue and driver drowsiness monitoring is an important subject for designing driver assistance systems. The measurement of eye closure is a fundamental step for driver awareness detection. We propose a method which is based on eyelid detection and the measurement of the distance between the eyelids. First, the face and the eyes of the driver are localized. After extracting the eye region, the proposed algorithm detects eyelids and computes the percentage of eye closure. Experimental results are performed on the BioID database. Our comparisons show that the proposed method outperforms state-of-the-art methods.

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References

  1. NHTSA (2009), http://www.nhtsa.dot.gov/

  2. Akrout, B., Mahdi, W.: A blinking measurement method for driver drowsiness detection. Advances in Intelligent Systems and Computing 226, 651–660 (2013)

    Article  Google Scholar 

  3. Daniluk, M., Rezaei, M., Nicolescu, R., Klette, R.: Monocular driver monitoring under different lighting conditions. Available in enpeda image sequence analysis test site (EISATS), Set 11 (2014), www.mi.auckland.ac.nz/EISATS

  4. Doyle, W.: Operations useful for similarity-invariant pattern recognition. J. ACM 9(2), 259–267 (1962)

    Article  MATH  Google Scholar 

  5. Horng, W.B., Chen, C.Y., Chang, Y., Fan, C.H.: Driver fatigue detection based on eye tracking and dynamic, template matching. In: Proc. Networking Sensing Control, vol. 1, pp. 7–12 (2004)

    Google Scholar 

  6. Jo, J., Jung, H.G., Park, K.R., Kim, J., Lee, S.J.: Vision-based method for detecting driver drowsiness and distraction in driver monitoring system. Optical Engineering 50(12), 127202 (2011)

    Article  Google Scholar 

  7. Klette, R.: Concise Computer Vision. Springer, London (2014)

    Google Scholar 

  8. Liu, A., Li, Z., Wang, L., Zhao, Y.: A practical driver fatigue detection algorithm based on eye state. In: Proc. Asia Pacific Conf. Postgraduate Research Microelectronics Electronics (PrimeAsia), pp. 235–238 (2010)

    Google Scholar 

  9. Malla, A.M., Davidson, P.R., Bones, P.J., Green, R., Jones, R.D.: Automated video-based measurement of eye closure for detecting behavioral microsleep. In: Proc. Engineering Medicine Biology Society (EMBC), pp. 6741–6744 (2010)

    Google Scholar 

  10. Omidyeganeh, M., Javadtalab, A., Shirmohammadi, S.: Intelligent driver drowsiness detection through fusion of yawning and eye closure. In: Proc. Virtual Environments Human-Computer Interfaces Measurement Systems (VECIMS), pp. 1–6 (2011)

    Google Scholar 

  11. Rezaei, M., Klette, R.: 3D cascade of classifiers for open and closed eye detection in driver distraction monitoring. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part II. LNCS, vol. 6855, pp. 171–179. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  12. Rezaei, M., Klette, R.: Adaptive Haar-like classifier for eye status detection under non-ideal lighting conditions. In: Proc. Image Vision Computing New Zealand, pp. 521–526 (2012)

    Google Scholar 

  13. Rezaei, M.: Artistic rendering of human portraits paying attention to facial features. In: Brooks, A.L. (ed.) ArtsIT 2011. LNICST, vol. 101, pp. 90–99. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  14. The BioID face database, http://www.bioid.com/downloads/facedb/facedatabase.html

  15. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. Computer Vision Pattern Recognition, pp. 511–518 (2001)

    Google Scholar 

  16. World Health Organization: WHO global status report on road safety 2013: supporting a decade of action (2013)

    Google Scholar 

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Daniluk, M., Rezaei, M., Nicolescu, R., Klette, R. (2014). Eye Status Based on Eyelid Detection: A Driver Assistance System. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-11331-9_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11330-2

  • Online ISBN: 978-3-319-11331-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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