Design and implementation of driver drowsiness detection system

There is a substantial amount of evidence that suggests that driver drowsiness
plays a significant role in road accidents. Alarming recent statistics are raising the
interest in equipping vehicles with driver drowsiness detection systems. This dissertation describes the design and implementation of a driver drowsiness detection system that is based on the analysis of visual input consisting of the driver's face and eyes. The resulting system combines off-the-shelf software components for face detection, human skin color detection and eye state classification in a novel way. It follows a behavioral methodology by performing a non-invasive monitoring of external cues describing a driver's level of drowsiness. We look at this complex problem from a
systems engineering point of view in order to go from a proof-of-concept prototype to
a stable software framework. Our system utilizes two detection and analysis methods:
(i) face detection with eye region extrapolation and (ii) eye state classification.
Additionally, we use two confirmation processes - one based on custom skin color
detection, the other based on nod detection - to make the system more robust and
resilient while not sacrificing speed significantly. The system was designed to be dynamic and adaptable to conform to the current conditions and hardware capabilities.