Automatic driver drowsiness detection using haar algorithm and support vector machine techniques

Ghassan Jasim Al-Anizy, Md. Jan Nordin, Mohammed M. Razooq

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Driver drowsiness is the most critical cause of traffic accidents, thus drowsiness detection play a vital role in preventing traffic accidents. By developing an automatic solution for alerting drivers of drowsing, before an accident occurs, this could reduce the number of traffic accidents. Therefore, this research proposes a real-time detection approach for driver drowsiness. The proposed approach has two phases: and machine learning. The role of phase is to recognize the face of the driver and then extracts the image of the eyes of the driver. This phase uses Haar face detection algorithm that takes captured frames of image as input and then the detected face as output. Next, Haar is also used to extract the eyes image from the detected face which will be used as an input for the machine learning phase. The main role of the machine learning is to classify either the eyes of the driver are closed or opened using Support Vector Machine (SVM). If the result of the classification indicates that the driver’s eyes is closed for a predefined period of time, the eyes of the driver will be considered closed and hence an alarm will be started to alert the driver. The proposed methodology has been tested on available benchmark data. The result demonstrates the accuracy and robustness of the hybridized of technique with machine learning technique. Thus, it can be concluded that the proposed approach is an effective solution method for a real-time of driver drowsiness detection.

Original languageEnglish
Pages (from-to)149-157
Number of pages9
JournalAsian Journal of Applied Sciences
Volume8
Issue number2
DOIs
Publication statusPublished - 2015

Fingerprint

Support vector machines
Highway accidents
Learning systems
Face recognition
Accidents

ASJC Scopus subject areas

  • General

Cite this

Automatic driver drowsiness detection using haar algorithm and support vector machine techniques. / Al-Anizy, Ghassan Jasim; Nordin, Md. Jan; Razooq, Mohammed M.

In: Asian Journal of Applied Sciences, Vol. 8, No. 2, 2015, p. 149-157.

Research output: Contribution to journalArticle

@article{9429e2a3162742e1aa6208dc23f1f17a,
title = "Automatic driver drowsiness detection using haar algorithm and support vector machine techniques",
abstract = "Driver drowsiness is the most critical cause of traffic accidents, thus drowsiness detection play a vital role in preventing traffic accidents. By developing an automatic solution for alerting drivers of drowsing, before an accident occurs, this could reduce the number of traffic accidents. Therefore, this research proposes a real-time detection approach for driver drowsiness. The proposed approach has two phases: and machine learning. The role of phase is to recognize the face of the driver and then extracts the image of the eyes of the driver. This phase uses Haar face detection algorithm that takes captured frames of image as input and then the detected face as output. Next, Haar is also used to extract the eyes image from the detected face which will be used as an input for the machine learning phase. The main role of the machine learning is to classify either the eyes of the driver are closed or opened using Support Vector Machine (SVM). If the result of the classification indicates that the driver’s eyes is closed for a predefined period of time, the eyes of the driver will be considered closed and hence an alarm will be started to alert the driver. The proposed methodology has been tested on available benchmark data. The result demonstrates the accuracy and robustness of the hybridized of technique with machine learning technique. Thus, it can be concluded that the proposed approach is an effective solution method for a real-time of driver drowsiness detection.",
author = "Al-Anizy, {Ghassan Jasim} and Nordin, {Md. Jan} and Razooq, {Mohammed M.}",
year = "2015",
doi = "10.3923/ajaps.2015.149.157",
language = "English",
volume = "8",
pages = "149--157",
journal = "Asian Journal of Applied Sciences",
issn = "1996-3343",
publisher = "Science Alert",
number = "2",

}

TY - JOUR

T1 - Automatic driver drowsiness detection using haar algorithm and support vector machine techniques

AU - Al-Anizy, Ghassan Jasim

AU - Nordin, Md. Jan

AU - Razooq, Mohammed M.

PY - 2015

Y1 - 2015

N2 - Driver drowsiness is the most critical cause of traffic accidents, thus drowsiness detection play a vital role in preventing traffic accidents. By developing an automatic solution for alerting drivers of drowsing, before an accident occurs, this could reduce the number of traffic accidents. Therefore, this research proposes a real-time detection approach for driver drowsiness. The proposed approach has two phases: and machine learning. The role of phase is to recognize the face of the driver and then extracts the image of the eyes of the driver. This phase uses Haar face detection algorithm that takes captured frames of image as input and then the detected face as output. Next, Haar is also used to extract the eyes image from the detected face which will be used as an input for the machine learning phase. The main role of the machine learning is to classify either the eyes of the driver are closed or opened using Support Vector Machine (SVM). If the result of the classification indicates that the driver’s eyes is closed for a predefined period of time, the eyes of the driver will be considered closed and hence an alarm will be started to alert the driver. The proposed methodology has been tested on available benchmark data. The result demonstrates the accuracy and robustness of the hybridized of technique with machine learning technique. Thus, it can be concluded that the proposed approach is an effective solution method for a real-time of driver drowsiness detection.

AB - Driver drowsiness is the most critical cause of traffic accidents, thus drowsiness detection play a vital role in preventing traffic accidents. By developing an automatic solution for alerting drivers of drowsing, before an accident occurs, this could reduce the number of traffic accidents. Therefore, this research proposes a real-time detection approach for driver drowsiness. The proposed approach has two phases: and machine learning. The role of phase is to recognize the face of the driver and then extracts the image of the eyes of the driver. This phase uses Haar face detection algorithm that takes captured frames of image as input and then the detected face as output. Next, Haar is also used to extract the eyes image from the detected face which will be used as an input for the machine learning phase. The main role of the machine learning is to classify either the eyes of the driver are closed or opened using Support Vector Machine (SVM). If the result of the classification indicates that the driver’s eyes is closed for a predefined period of time, the eyes of the driver will be considered closed and hence an alarm will be started to alert the driver. The proposed methodology has been tested on available benchmark data. The result demonstrates the accuracy and robustness of the hybridized of technique with machine learning technique. Thus, it can be concluded that the proposed approach is an effective solution method for a real-time of driver drowsiness detection.

UR - http://www.scopus.com/inward/record.url?scp=84921913506&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84921913506&partnerID=8YFLogxK

U2 - 10.3923/ajaps.2015.149.157

DO - 10.3923/ajaps.2015.149.157

M3 - Article

AN - SCOPUS:84921913506

VL - 8

SP - 149

EP - 157

JO - Asian Journal of Applied Sciences

JF - Asian Journal of Applied Sciences

SN - 1996-3343

IS - 2

ER -