Evaluating the effectiveness of facial actions features for the early detection of driver drowsiness in driving safety monitoring system

Traffic accidents caused by drowsiness remain a serious threat to driving safety. Many of these accidents can actually be prevented with an early warning system that detects the early signs of driver drowsiness. This study proposes a non-invasive system to detect drowsiness based on visual features extracted from videos recorded by a dashboard camera. The system uses facial landmarks generated by a facial network detector to identify key areas such as eyes, mouth, and head. The eye aspect ratio (EAR), mouth aspect ratio (MAR), and head rotation angle were calculated as the main features. These features were fed into three classification models: 1D-CNN, LSTM, and BiLSTM. Evaluation was conducted using 87 videos from the YawDD dataset for training and 20 videos from custom data for testing. During training, the 5-fold cross-validation was used to ensure model generalization and reduce the risk of overfitting. In addition to accuracy, other metrics such as precision, recall, and F1-score were used to provide a more comprehensive overview of the system performance. The results showed that the combination of the three facial features (EAR, MAR, and head rotation) provided a better performance than did the use of a single feature or a combination of two features, with an accuracy improvement of 5–8%. The BiLSTM model showed the best performance, with a training accuracy of 99% on the YawDD dataset and a testing accuracy of 98% on the custom data.