Drowsiness Detection in Drivers Using Facial Feature Analysis
Drowsiness has been recognized as a leading factor in road accidents worldwide. Despite considerable research in this area, this paper aims to improve the precision of drowsiness detection specifically for long-haul travel by employing the Dlib-based facial feature detection algorithm. This study pr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/20 |
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| _version_ | 1841549477393989632 |
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| author | Ebenezer Essel Fred Lacy Fatema Albalooshi Wael Elmedany Yasser Ismail |
| author_facet | Ebenezer Essel Fred Lacy Fatema Albalooshi Wael Elmedany Yasser Ismail |
| author_sort | Ebenezer Essel |
| collection | DOAJ |
| description | Drowsiness has been recognized as a leading factor in road accidents worldwide. Despite considerable research in this area, this paper aims to improve the precision of drowsiness detection specifically for long-haul travel by employing the Dlib-based facial feature detection algorithm. This study proposes two algorithms: a static and adaptive frame threshold. Both approaches utilize eye closure ratio (ECR) and mouth aperture ratio (MAR) parameters to determine the driver’s level of drowsiness. The static threshold method issues a warning when the ECR and/or MAR values reach specific thresholds. In this method, the ECR threshold is established at 0.15 and the MAR threshold at 0.4. The static threshold method demonstrated an accuracy of 89.4% and a sensitivity of 96.5% using 1000 images. The adaptive frame threshold algorithm uses a counter to monitor the number of consecutive frames that meet the drowsiness criteria before triggering a warning. Additionally, the number of consecutive frames required is adjusted dynamically over time to enhance detection accuracy and more accurately indicate a state of drowsiness. The adaptive frame threshold algorithm was tested using four 30 min videos, from a publicly available dataset achieving a maximum accuracy of 98.2% and a sensitivity of 64.3% with 500 images. |
| format | Article |
| id | doaj-art-0c884487fc5e4a03a6fb9be50608881c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0c884487fc5e4a03a6fb9be50608881c2025-01-10T13:14:11ZengMDPI AGApplied Sciences2076-34172024-12-011512010.3390/app15010020Drowsiness Detection in Drivers Using Facial Feature AnalysisEbenezer Essel0Fred Lacy1Fatema Albalooshi2Wael Elmedany3Yasser Ismail4Department of Electrical Engineering, Louisiana State University, Baton Rouge, LA 70803, USADepartment of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USACollege of Information Technology, University of Bahrain, Zallaq 1054, BahrainCollege of Information Technology, University of Bahrain, Zallaq 1054, BahrainDepartment of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USADrowsiness has been recognized as a leading factor in road accidents worldwide. Despite considerable research in this area, this paper aims to improve the precision of drowsiness detection specifically for long-haul travel by employing the Dlib-based facial feature detection algorithm. This study proposes two algorithms: a static and adaptive frame threshold. Both approaches utilize eye closure ratio (ECR) and mouth aperture ratio (MAR) parameters to determine the driver’s level of drowsiness. The static threshold method issues a warning when the ECR and/or MAR values reach specific thresholds. In this method, the ECR threshold is established at 0.15 and the MAR threshold at 0.4. The static threshold method demonstrated an accuracy of 89.4% and a sensitivity of 96.5% using 1000 images. The adaptive frame threshold algorithm uses a counter to monitor the number of consecutive frames that meet the drowsiness criteria before triggering a warning. Additionally, the number of consecutive frames required is adjusted dynamically over time to enhance detection accuracy and more accurately indicate a state of drowsiness. The adaptive frame threshold algorithm was tested using four 30 min videos, from a publicly available dataset achieving a maximum accuracy of 98.2% and a sensitivity of 64.3% with 500 images.https://www.mdpi.com/2076-3417/15/1/20driver fatigueEye Closure RatioMouth Aperture Ratiofacial landmark detectionstatic thresholdadaptive threshold |
| spellingShingle | Ebenezer Essel Fred Lacy Fatema Albalooshi Wael Elmedany Yasser Ismail Drowsiness Detection in Drivers Using Facial Feature Analysis Applied Sciences driver fatigue Eye Closure Ratio Mouth Aperture Ratio facial landmark detection static threshold adaptive threshold |
| title | Drowsiness Detection in Drivers Using Facial Feature Analysis |
| title_full | Drowsiness Detection in Drivers Using Facial Feature Analysis |
| title_fullStr | Drowsiness Detection in Drivers Using Facial Feature Analysis |
| title_full_unstemmed | Drowsiness Detection in Drivers Using Facial Feature Analysis |
| title_short | Drowsiness Detection in Drivers Using Facial Feature Analysis |
| title_sort | drowsiness detection in drivers using facial feature analysis |
| topic | driver fatigue Eye Closure Ratio Mouth Aperture Ratio facial landmark detection static threshold adaptive threshold |
| url | https://www.mdpi.com/2076-3417/15/1/20 |
| work_keys_str_mv | AT ebenezeressel drowsinessdetectionindriversusingfacialfeatureanalysis AT fredlacy drowsinessdetectionindriversusingfacialfeatureanalysis AT fatemaalbalooshi drowsinessdetectionindriversusingfacialfeatureanalysis AT waelelmedany drowsinessdetectionindriversusingfacialfeatureanalysis AT yasserismail drowsinessdetectionindriversusingfacialfeatureanalysis |