  Driver drowsiness detection using videos/images is one of the most essential
areas in today's time for driver safety. The development of deep learning
techniques, notably Convolutional Neural Networks (CNN), applied in computer
vision applications such as drowsiness detection, has shown promising results
due to the tremendous increase in technology in the recent few decades. Eyes
that are closed or blinking excessively, yawning, nodding, and occlusion are
all key aspects of drowsiness. In this work, we have applied four different
Convolutional Neural Network (CNN) techniques on the YawDD dataset to detect
and examine the extent of drowsiness depending on the yawning frequency with
specific pose and occlusion variation. Preliminary computational results show
that our proposed Ensemble Convolutional Neural Network (ECNN) outperformed the
traditional CNN-based approach by achieving an F1 score of 0.935, whereas the
other three CNN, such as CNN1, CNN2, and CNN3 approaches gained 0.92, 0.90, and
0.912 F1 scores, respectively.
