Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob
Driver drowsiness or fatigue is a significant factor that causes road accidents each year and considerably affects road safety. According to the World Health Organization (WHO), drowsy driving may contribute to approximately 6% of fatal and severe road accidents. To overcome this problem, we present...
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| Language: | en |
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Universiti Teknologi MARA
2023
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| Online Access: | https://ir.uitm.edu.my/id/eprint/77346/1/77346.pdf https://ir.uitm.edu.my/id/eprint/77346/ https://mjoc.uitm.edu.my/main/ |
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| _version_ | 1833076548973887488 |
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| author | Gupta, Nipa Das Rajoo, Rajesvary Jacob, Patricia Jayshree |
| author_facet | Gupta, Nipa Das Rajoo, Rajesvary Jacob, Patricia Jayshree |
| author_sort | Gupta, Nipa Das |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Driver drowsiness or fatigue is a significant factor that causes road accidents each year and considerably affects road safety. According to the World Health Organization (WHO), drowsy driving may contribute to approximately 6% of fatal and severe road accidents. To overcome this problem, we present a state-of-the-art, real-time drowsiness detection system, which exploits innovative deep-learning techniques to evaluate facial expressions. Our system analyzes not just the driver's eyes, mouth, and head rotation pose with front angles but also left and right yaw angles up to 90° to ensure the driver's safety. We gathered a dataset from public stock image websites, and manual image captures to develop the system. After processing the dataset, we extracted a wide range of features, which we fed into a deep convolutional neural network (CNN) algorithm. Specifically, we employed three different CNN algorithms which are EfficientDet D0, SSD MobileNet V2, and SSD ResNet50 V1, to classify the driver's drowsiness status using the facial key attributes in real time. Our results show that the SSD ResNet50 V1 model exhibited the highest accuracy and consistency in detecting driver drowsiness, underscoring the potential of our innovative system in promoting road safety. Our future work will focus on fine-tuning the approach to enhance its accuracy and performance. |
| format | Article |
| id | my.uitm.ir-77346 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2023 |
| publisher | Universiti Teknologi MARA |
| record_format | eprints |
| spelling | my.uitm.ir-773462023-05-09T08:41:34Z https://ir.uitm.edu.my/id/eprint/77346/ Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob mjoc Gupta, Nipa Das Rajoo, Rajesvary Jacob, Patricia Jayshree Human face recognition (Computer science) Driver drowsiness or fatigue is a significant factor that causes road accidents each year and considerably affects road safety. According to the World Health Organization (WHO), drowsy driving may contribute to approximately 6% of fatal and severe road accidents. To overcome this problem, we present a state-of-the-art, real-time drowsiness detection system, which exploits innovative deep-learning techniques to evaluate facial expressions. Our system analyzes not just the driver's eyes, mouth, and head rotation pose with front angles but also left and right yaw angles up to 90° to ensure the driver's safety. We gathered a dataset from public stock image websites, and manual image captures to develop the system. After processing the dataset, we extracted a wide range of features, which we fed into a deep convolutional neural network (CNN) algorithm. Specifically, we employed three different CNN algorithms which are EfficientDet D0, SSD MobileNet V2, and SSD ResNet50 V1, to classify the driver's drowsiness status using the facial key attributes in real time. Our results show that the SSD ResNet50 V1 model exhibited the highest accuracy and consistency in detecting driver drowsiness, underscoring the potential of our innovative system in promoting road safety. Our future work will focus on fine-tuning the approach to enhance its accuracy and performance. Universiti Teknologi MARA 2023-04 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/77346/1/77346.pdf Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob. (2023) Malaysian Journal of Computing (MJoC) <https://ir.uitm.edu.my/view/publication/Malaysian_Journal_of_Computing_=28MJoC=29/>, 8 (1): 9. pp. 1375-1387. ISSN 2600-8238 https://mjoc.uitm.edu.my/main/ |
| spellingShingle | Human face recognition (Computer science) Gupta, Nipa Das Rajoo, Rajesvary Jacob, Patricia Jayshree Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob |
| title | Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob |
| title_full | Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob |
| title_fullStr | Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob |
| title_full_unstemmed | Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob |
| title_short | Driver drowsiness detection system through facial expression using Convolutional Neural Networks (CNN) / Nipa Das Gupta, Rajesvary Rajoo and Patricia Jayshree Jacob |
| title_sort | driver drowsiness detection system through facial expression using convolutional neural networks (cnn) / nipa das gupta, rajesvary rajoo and patricia jayshree jacob |
| topic | Human face recognition (Computer science) |
| url | https://ir.uitm.edu.my/id/eprint/77346/1/77346.pdf https://ir.uitm.edu.my/id/eprint/77346/ https://mjoc.uitm.edu.my/main/ |
| url_provider | http://ir.uitm.edu.my/ |