A novel drowsiness detection model using composite features of head, eye, and facial expression

Drowsiness is the principal cause of road crashes nowadays, as per the existing data. Drowsiness may put many precious lives in jeopardy. Drowsiness may be detected early and accurately, which can save lives. Using computer vision and deep learning techniques, this research proposes a new approach to detect driver drowsiness at an early stage with improved accuracy. In our developed model, we have considered the most significant temporal features such as head pose angles (Yaw, Pitch, and Roll), centers of pupil movement, and distance for the emotional feature that help in the detection of drowsiness state more accurately. Our method solves the possibility of occluded frames at initial stage via imposing the occlusion criteria depending on the relationship of distance between pupil centers and the horizontal length of the eye. As a result, it outperformed existing approaches in terms of overall system accuracy and consistency. Furthermore, retrieved features from correct frames are used as training and test data by the long short-term memory network to classify the driver's state. Here, results are elaborated in terms of area under the curve-receiver operating characteristic curve scores.

Automated summary

Drowsiness is a major cause of road accidents, but it can be detected early and accurately using computer vision and deep learning techniques, potentially saving lives.