The increase in accident and death rates due to drowsiness while driving raises concerns to the community. An efficient solution is vital to ensure the safety of all drivers on the road. Most previous studies have analyzed drowsiness using head tilt, yawning, and eye condition. Face detection applied in drowsiness detection from previous research not included distances between subject and camera. The features used for eye detection required large storage and long-term process which are not applicable in a real-time system. This study uses Haar algorithm and analysis is performed based on the size of the region of interest for face detection. Eye monitoring uses facial landmark features and the evaluation is dependent on the width of the eye. The percentage of eye closure is used to describe the eyes as closed. This study only takes into account the normal rate of blinking eyes while driving because of the long-time constraints required for a person to be in a drowsy state. In this research, the Raspberry Pi 3B+ and Pi cameras are used as processing and vision devices. The highest accuracy of face detection achieved based on the ROI area at a distance of 80 cm is 98.33%. The lowest difference between eye width and the intercanthal distance is 0.36%. The overall normal eye blink rate while driving is in the range of the normal eye blink rate which does not exceed 20 blinks/min as reported by the previous researcher.
