Abstract:

Drowsiness detection is crucial for ensuring safety across various sectors, including transportation, healthcare, and industrial settings. This paper introduces a novel approach to realtime drowsiness detection utilizing facial landmarks and machine learning algorithms. The system focuses on two primary indicators of drowsiness: the Eye Aspect Ratio (EAR) and yawning patterns(MAR), which are analyzed using deep learning techniques to process facial landmarks extracted from video streams

Description:

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