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Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

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posted on 20.05.2020 by Arafat Al-Dweik, Reza Mohammadi Tamanani, Radu Muresan
Road accidents caused by human error are among
the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,
a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method
for practical applications in intelligent transportation systems

History

Email Address of Submitting Author

dweik@fulbrightmail.org

ORCID of Submitting Author

0000-0002-3487-3438

Submitting Author's Institution

Khalifa University

Submitting Author's Country

United Arab Emirates

Licence

Exports