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