Modern cities have imposed a fast-paced lifestyle where more drivers on the road suffer
from fatigue and sleep deprivation. Consequently, road accidents have increased, becoming one of
the leading causes of injuries and death among young adults and children. These accidents can be
prevented if fatigue symptoms are diagnosed and detected sufficiently early. For this reason, we
propose and compare two AlexNet CNN-based models to detect drivers’ fatigue behaviors, relying
on head position and mouth movements as behavioral measures. We used two different approaches.
The first approach is transfer learning, specifically, fine-tuning AlexNet, which allowed us to take
advantage of what the model had already learned without developing it from scratch. The newly
trained model was able to predict drivers’ drowsiness behaviors. The second approach is the use of
AlexNet to extract features by training the top layers of the network. These features were reduced
using non-negative matrix factorization (NMF) and classified with a support vector machine (SVM)
classifier. The experiments showed that our proposed transfer learning model achieved an accuracy
of 95.7%, while the feature extraction SVM-based model performed better, with an accuracy of 99.65%.
Both models were trained on a simulated NTHU Driver Drowsiness Detection datase