research-article

Driver drowsiness estimation using facial wrinkle feature

Authors:

Taro Nakamura,

Tatsuhide Matsuda,

Akinobu Maejima,

Shigeo MorishimaAuthors Info & Claims

SIGGRAPH '13: ACM SIGGRAPH 2013 Posters

Article No.: 30, Page 1

https://doi.org/10.1145/2503385.2503418

Published: 21 July 2013 Publication History

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Abstract

In recent years, the rate of fatal motor vehicle accidents caused by distracted driving resulting from factors such as sleeping at the wheel has been increasing. Therefore, an alert system that detects driver drowsiness and prevents accidents as a result by warning drivers before they fall asleep is urgently required. Non-contact measuring systems using computer vision techniques have been studied, and in vision approach, it is important to decide what kind of feature we should use for estimating drowsiness.

References

[1]

Hachisuka, S., Kimura, T., Ozaki, N., Ishida, K., and Nakatani, H. 2010. Drowsiness Detection Using Facial Expression Features. SAE International paper. 2010-01-0466.

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[2]

Irie, A., Takagiwa, M., Moriyama, K., and Yamashita, T. 2011. Improvements to Facial Contour Detection by Hierarchical Fitting and Regression. The First Asian Conference on Pattern Recognition Oral. pp. 273--277.

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WONG IPOPKIN SFOLKARD S(2019)Working Time Society consensus statements: A multi-level approach to managing occupational sleep-related fatigueIndustrial Health10.2486/indhealth.SW-657:2(228-244)Online publication date: 2019
https://doi.org/10.2486/indhealth.SW-6
Kang H(2014)Monitoring Driver’s State and Predicting Unsafe Driving BehaviorAlgorithm & SoC Design for Automotive Vision Systems10.1007/978-94-017-9075-8_6(143-161)Online publication date: 29-Jun-2014
https://doi.org/10.1007/978-94-017-9075-8_6
Kang H(2013)Various Approaches for Driver and Driving Behavior MonitoringProceedings of the 2013 IEEE International Conference on Computer Vision Workshops10.1109/ICCVW.2013.85(616-623)Online publication date: 2-Dec-2013
https://dl.acm.org/doi/10.1109/ICCVW.2013.85

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This paper presents the method of detecting driver's drowsiness level from facial expressions. Our method is executed according to the following flow: taking a driver's facial image, tracing the facial features by image processing, and classifying the ...

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SIGGRAPH '13: ACM SIGGRAPH 2013 Posters

July 2013

115 pages

ISBN:9781450323420

DOI:10.1145/2503385

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 21 July 2013

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SIGGRAPH '13: Special Interest Group on Computer Graphics and Interactive Techniques Conference

July 21 - 25, 2013

California, Anaheim

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Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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WONG IPOPKIN SFOLKARD S(2019)Working Time Society consensus statements: A multi-level approach to managing occupational sleep-related fatigueIndustrial Health10.2486/indhealth.SW-657:2(228-244)Online publication date: 2019
https://doi.org/10.2486/indhealth.SW-6
Kang H(2014)Monitoring Driver’s State and Predicting Unsafe Driving BehaviorAlgorithm & SoC Design for Automotive Vision Systems10.1007/978-94-017-9075-8_6(143-161)Online publication date: 29-Jun-2014
https://doi.org/10.1007/978-94-017-9075-8_6
Kang H(2013)Various Approaches for Driver and Driving Behavior MonitoringProceedings of the 2013 IEEE International Conference on Computer Vision Workshops10.1109/ICCVW.2013.85(616-623)Online publication date: 2-Dec-2013
https://dl.acm.org/doi/10.1109/ICCVW.2013.85

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Driver drowsiness in automated and manual driving: insights from a test track study

Detecting Driver Drowsiness Using Wireless Wearables

Facial expression measurement for detecting driver drowsiness

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