{"id":{"repo_id":"oxford-brookes","oai_identifier":"tle:a283f91a-fcc6-4712-bc3c-b404530c598c:d6bd9758-527a-46cd-bfe2-c433766e8fca:1"},"canonical_url":"https://search.dev.ndltd.org/etd/oxford-brookes/tle:a283f91a-fcc6-4712-bc3c-b404530c598c:d6bd9758-527a-46cd-bfe2-c433766e8fca:1","repository":{"repo_id":"oxford-brookes","name":"Oxford Brookes University","base_url":"https://radar.brookes.ac.uk/radar/oai"},"display":{"title":"Artificial Intelligence Techniques for Driver Fatigue Detection","abstract":"The research discussed here aims to design a deep learning algorithm based on Convolutional Neural Networks models to detect driver distraction and fatigue using driver facial expressions. The proposed model provides high accuracy during both training and validation. The research was inspired to contribute to transport safety by providing alternative solutions to detect driver habit. First, the thesis discussed Conventional methods, including Haar cascade classifiers and eigenfaces. In 2018 I published a proposal for a blink rate detection system using Haar cascade feature detection. However, due to the advantages of Neural Networks, the research focused on providing a unique solution in that field. An in-depth look at how Neural Networks function, specifically Convolutional Neural Networks (CNNs), was investigated and discussed next. Due to the advantages CNN's have with feature detection in images, the algorithm I proposed in this research uses a CNN architecture. Lastly, I proposed an adaptive approach for deep learning to enhance training, validation and testing accuracies. My original algorithm and subsequent models were trained on two datasets. These were the American University in Cairo (AUC) Distracted Driver Dataset and the UTA Real-Life Drowsiness Dataset (UTA-RLDD). Hence the research proposed two original CNN models that produced high training and validation accuracy. The model designed on the AUC Distracted Driver Dataset achieved good 97% training accuracy and good 96% validation accuracy. Evaluation of this model produced good 99% accuracy. The model designed on UTA-RLDD achieved 100% training accuracy, 69% validation accuracy, and evaluated at 69% accuracy.","abstract_html":"The research discussed here aims to design a deep learning algorithm based on Convolutional Neural Networks models to detect driver distraction and fatigue using driver facial expressions. The proposed model provides high accuracy during both training and validation. The research was inspired to contribute to transport safety by providing alternative solutions to detect driver habit. First, the thesis discussed Conventional methods, including Haar cascade classifiers and eigenfaces. In 2018 I published a proposal for a blink rate detection system using Haar cascade feature detection. However, due to the advantages of Neural Networks, the research focused on providing a unique solution in that field. An in-depth look at how Neural Networks function, specifically Convolutional Neural Networks (CNNs), was investigated and discussed next. Due to the advantages CNN's have with feature detection in images, the algorithm I proposed in this research uses a CNN architecture. Lastly, I proposed an adaptive approach for deep learning to enhance training, validation and testing accuracies. My original algorithm and subsequent models were trained on two datasets. These were the American University in Cairo (AUC) Distracted Driver Dataset and the UTA Real-Life Drowsiness Dataset (UTA-RLDD). Hence the research proposed two original CNN models that produced high training and validation accuracy. The model designed on the AUC Distracted Driver Dataset achieved good 97% training accuracy and good 96% validation accuracy. Evaluation of this model produced good 99% accuracy. The model designed on UTA-RLDD achieved 100% training accuracy, 69% validation accuracy, and evaluated at 69% accuracy.","abstract_has_math":false,"creators":["Yassine, Nabil"],"institution":"Oxford Brookes University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Hayatleh, Khaled","Barker, Steve","Choubey, Bhaskar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-24T03:43:42Z","subjects":[],"languages":["en"],"rights":["All rights reserved"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.24384/6cdr-ba11","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yassine, Nabil","Hayatleh, Khaled","Barker, Steve","Choubey, Bhaskar"]},{"key":"dc:creator","label":"Author","values":["Yassine, Nabil"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020"]},{"key":"dc:publisher","label":"Institution","values":["Oxford Brookes University"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["All rights reserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.24384/6cdr-ba11","https://radar.brookes.ac.uk/radar/file/a283f91a-fcc6-4712-bc3c-b404530c598c/1/Yassine2020DriverFatigue.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The research discussed here aims to design a deep learning algorithm based on Convolutional Neural Networks models to detect driver distraction and fatigue using driver facial expressions. The proposed model provides high accuracy during both training and validation. The research was inspired to contribute to transport safety by providing alternative solutions to detect driver habit. First, the thesis discussed Conventional methods, including Haar cascade classifiers and eigenfaces. In 2018 I published a proposal for a blink rate detection system using Haar cascade feature detection. However, due to the advantages of Neural Networks, the research focused on providing a unique solution in that field. An in-depth look at how Neural Networks function, specifically Convolutional Neural Networks (CNNs), was investigated and discussed next. Due to the advantages CNN's have with feature detection in images, the algorithm I proposed in this research uses a CNN architecture. Lastly, I proposed an adaptive approach for deep learning to enhance training, validation and testing accuracies. My original algorithm and subsequent models were trained on two datasets. These were the American University in Cairo (AUC) Distracted Driver Dataset and the UTA Real-Life Drowsiness Dataset (UTA-RLDD). Hence the research proposed two original CNN models that produced high training and validation accuracy. The model designed on the AUC Distracted Driver Dataset achieved good 97% training accuracy and good 96% validation accuracy. Evaluation of this model produced good 99% accuracy. The model designed on UTA-RLDD achieved 100% training accuracy, 69% validation accuracy, and evaluated at 69% accuracy."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Artificial Intelligence Techniques for Driver Fatigue Detection"]}]}],"canonical_facts":{"dc:contributor":["Yassine, Nabil","Hayatleh, Khaled","Barker, Steve","Choubey, Bhaskar"],"dc:creator":["Yassine, Nabil"],"dc:date":["2020"],"dc:description":["The research discussed here aims to design a deep learning algorithm based on Convolutional Neural Networks models to detect driver distraction and fatigue using driver facial expressions. The proposed model provides high accuracy during both training and validation. The research was inspired to contribute to transport safety by providing alternative solutions to detect driver habit. First, the thesis discussed Conventional methods, including Haar cascade classifiers and eigenfaces. In 2018 I published a proposal for a blink rate detection system using Haar cascade feature detection. However, due to the advantages of Neural Networks, the research focused on providing a unique solution in that field. An in-depth look at how Neural Networks function, specifically Convolutional Neural Networks (CNNs), was investigated and discussed next. Due to the advantages CNN's have with feature detection in images, the algorithm I proposed in this research uses a CNN architecture. Lastly, I proposed an adaptive approach for deep learning to enhance training, validation and testing accuracies. My original algorithm and subsequent models were trained on two datasets. These were the American University in Cairo (AUC) Distracted Driver Dataset and the UTA Real-Life Drowsiness Dataset (UTA-RLDD). Hence the research proposed two original CNN models that produced high training and validation accuracy. The model designed on the AUC Distracted Driver Dataset achieved good 97% training accuracy and good 96% validation accuracy. Evaluation of this model produced good 99% accuracy. The model designed on UTA-RLDD achieved 100% training accuracy, 69% validation accuracy, and evaluated at 69% accuracy."],"dc:format":["application/pdf"],"dc:identifier":["https://doi.org/10.24384/6cdr-ba11","https://radar.brookes.ac.uk/radar/file/a283f91a-fcc6-4712-bc3c-b404530c598c/1/Yassine2020DriverFatigue.pdf"],"dc:language":["en"],"dc:publisher":["Oxford Brookes University"],"dc:rights":["All rights reserved"],"dc:title":["Artificial Intelligence Techniques for Driver Fatigue Detection"],"dc:type":["thesis"]},"updated_at":"2026-07-24T03:43:42Z"}