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Measuring driver cognitive distraction through lips and eyebrows

Cognitive distraction is one of the several contributory factors in road accidents. A number of cognitive distraction detection methods have been developed. One of the most popular methods is based on physiological measurement. Head orientation, gaze rotation, blinking and pupil diameter are among p...

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Main Authors: Azman, Afizan, Abdullah, Mohd. Fikri Azli, Yogarayan, Sumendra, Abdul Razak, Siti Fatimah, Muthu, Kalaiarasi Sonai, Hani Suhaila, Azman, Hartini
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26226/2/MEASURING%20DRIVER%20COGNITIVE%20DISTRACTION%20THROUGH%20LIPS%20AND%20EYEBROWS.PDF
http://eprints.utem.edu.my/id/eprint/26226/
https://ijece.iaescore.com/index.php/IJECE/article/view/25522/15382
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spelling my.utem.eprints.262262023-03-02T12:13:56Z http://eprints.utem.edu.my/id/eprint/26226/ Measuring driver cognitive distraction through lips and eyebrows Azman, Afizan Abdullah, Mohd. Fikri Azli Yogarayan, Sumendra Abdul Razak, Siti Fatimah Muthu, Kalaiarasi Sonai Hani Suhaila Azman, Hartini Cognitive distraction is one of the several contributory factors in road accidents. A number of cognitive distraction detection methods have been developed. One of the most popular methods is based on physiological measurement. Head orientation, gaze rotation, blinking and pupil diameter are among popular physiological parameters that are measured for driver cognitive distraction. In this paper, lips and eyebrows are studied. These new features on human facial expression are obvious and can be easily measured when a person is in cognitive distraction. There are several types of movement on lips and eyebrows that can be captured to indicate cognitive distraction. Correlation and classification techniques are used in this paper for performance measurement and comparison. Real time driving experiment was setup and faceAPI was installed in the car to capture driver's facial expression. Linear regression, support vector machine (SVM), static Bayesian network (SBN) and logistic regression (LR) are used in this study. Results showed that lips and eyebrows are strongly correlated and have a significant role in improving cognitive distraction detection. Dynamic Bayesian network (DBN) with different confidence of levels was also used in this study to classify whether a driver is distracted or not. Institute of Advanced Engineering and Science 2022-02 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26226/2/MEASURING%20DRIVER%20COGNITIVE%20DISTRACTION%20THROUGH%20LIPS%20AND%20EYEBROWS.PDF Azman, Afizan and Abdullah, Mohd. Fikri Azli and Yogarayan, Sumendra and Abdul Razak, Siti Fatimah and Muthu, Kalaiarasi Sonai and Hani Suhaila and Azman, Hartini (2022) Measuring driver cognitive distraction through lips and eyebrows. International Journal of Electrical and Computer Engineering, 12 (1). pp. 756-769. ISSN 2088-8708 https://ijece.iaescore.com/index.php/IJECE/article/view/25522/15382 10.11591/ijece.v12i1.pp756-769
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Cognitive distraction is one of the several contributory factors in road accidents. A number of cognitive distraction detection methods have been developed. One of the most popular methods is based on physiological measurement. Head orientation, gaze rotation, blinking and pupil diameter are among popular physiological parameters that are measured for driver cognitive distraction. In this paper, lips and eyebrows are studied. These new features on human facial expression are obvious and can be easily measured when a person is in cognitive distraction. There are several types of movement on lips and eyebrows that can be captured to indicate cognitive distraction. Correlation and classification techniques are used in this paper for performance measurement and comparison. Real time driving experiment was setup and faceAPI was installed in the car to capture driver's facial expression. Linear regression, support vector machine (SVM), static Bayesian network (SBN) and logistic regression (LR) are used in this study. Results showed that lips and eyebrows are strongly correlated and have a significant role in improving cognitive distraction detection. Dynamic Bayesian network (DBN) with different confidence of levels was also used in this study to classify whether a driver is distracted or not.
format Article
author Azman, Afizan
Abdullah, Mohd. Fikri Azli
Yogarayan, Sumendra
Abdul Razak, Siti Fatimah
Muthu, Kalaiarasi Sonai
Hani Suhaila
Azman, Hartini
spellingShingle Azman, Afizan
Abdullah, Mohd. Fikri Azli
Yogarayan, Sumendra
Abdul Razak, Siti Fatimah
Muthu, Kalaiarasi Sonai
Hani Suhaila
Azman, Hartini
Measuring driver cognitive distraction through lips and eyebrows
author_facet Azman, Afizan
Abdullah, Mohd. Fikri Azli
Yogarayan, Sumendra
Abdul Razak, Siti Fatimah
Muthu, Kalaiarasi Sonai
Hani Suhaila
Azman, Hartini
author_sort Azman, Afizan
title Measuring driver cognitive distraction through lips and eyebrows
title_short Measuring driver cognitive distraction through lips and eyebrows
title_full Measuring driver cognitive distraction through lips and eyebrows
title_fullStr Measuring driver cognitive distraction through lips and eyebrows
title_full_unstemmed Measuring driver cognitive distraction through lips and eyebrows
title_sort measuring driver cognitive distraction through lips and eyebrows
publisher Institute of Advanced Engineering and Science
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26226/2/MEASURING%20DRIVER%20COGNITIVE%20DISTRACTION%20THROUGH%20LIPS%20AND%20EYEBROWS.PDF
http://eprints.utem.edu.my/id/eprint/26226/
https://ijece.iaescore.com/index.php/IJECE/article/view/25522/15382
_version_ 1759693057924530176
score 13.211869

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