Drowsiness Detection System Through Eye and Mouth Analysis


Lim, Belle Bey Ee and Ng, Kok Why and Ng, Sew Lai (2023) Drowsiness Detection System Through Eye and Mouth Analysis. JOIV : International Journal on Informatics Visualization, 7 (4). p. 2489. ISSN 2549-9610

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Traffic jams are one of the serious issues in many developed countries. After the pandemic, many employees were allowed to travel interstate to work. This contributes to more severe jams, especially in the capital and nearby states. Long-distance driving and congestion can easily make the drivers sleepy and thus lead to traffic accidents. This paper aims to study and analyze facial cues to detect early symptoms of drowsy driving. The proposed method employs a deep learning approach, utilizing ensemble CNNs and Dlib's 68 landmark face detectors to analyze the facial cues. The analyzed symptoms include the frequency of eyes opened or closed and yawning or no yawning. Three individual CNN models and an ensemble CNN structure are built for the classification of the eyes and mouth yawn. The model training and validation accuracy graph and training loss and validation loss graph are plotted to verify that the models have not been overfitted. The ensemble CNN models achieved an approximate accuracy of 97.4% from the eyes and 96.5% from the mouth. It outperforms the other pre-trained models. The proposed system can immediately alert the driver and send text drowsy messages and emails to the third party, ensuring timely intervention to prevent accidents. The proposed method can be integrated into vehicles and transportation systems to ensure driver's safety. It can also be applied to monitor the driving behavior of those who drive long distances

Item Type: Article
Uncontrolled Keywords: Drowsiness Detection System; Driver Monitoring; Facial Expression Recognition; Ensemble CNN; Image Processing.
Subjects: Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 23 Feb 2024 04:02
Last Modified: 23 Feb 2024 04:02
URII: http://shdl.mmu.edu.my/id/eprint/12120


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