Eye Aspect Ratio for Real-Time Drowsiness Detection to Improve Driver Safety

Drowsiness is a major risk factor for road safety, contributing to serious injury, death, and economic loss on the road. Driving performance decreases because of increased drowsiness. In several different applications, such as facial movement analysis driver blink detection an essential requirement that used. The extremely rapid rate, other hand, makes automatic challenging task. This research paper presents technique identifying eye blinks in video series recorded by car dashboard camera real time. suggested determines landmark positions each frame then extracts vertical distance between eyelids from positions. algorithm has been proposed estimates positions, single scalar quantity making use Eye Aspect Ratio (EAR), identifies closeness frame. end, are recognized employing modified EAR threshold value conjunction with pattern values relatively short period Experimental evidence indicates greater threshold, worse AUC’s accuracy performance. Further, 0.18 was determined be optimum our research.