Drowsiness Detection using EAR (Eye Aspect Ratio) by Machine Learning

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Drowsiness detection is critical in many sectors, including transportation, healthcare, and workplace safety, since it may have a substantial influence on human performance and safety. Traditional sleepiness detection approaches are frequently subjective, time intensive, and unsuitable for real-time applications. In recent years, computer vision-based techniques that use eye-related characteristics to identify tiredness have shown promise. The eye aspect ratio, a geometric measure determined from ocular landmarks that indicates the openness or closure of the eyes, is one such trait. We present a sleepiness detection method in this research that blends eye aspect ratio computation with machine learning techniques to provide real-time and accurate drowsiness evaluation. We offer a thorough technique that includes calculating the eye-aspect ratio, extracting features, and classifying them with machine learning methods. The ability of our proposed technique is evaluated using a dataset of eye pictures acquired from individuals under various sleepiness situations. Our experimental findings show that our technique is successful in detecting sleepiness with good accuracy, sensitivity, and specificity. Our suggested method includes applications such as sleepy driving detection, tiredness monitoring in hospital settings, and workplace safety. This research advances the area of sleepiness detection by combining the ocular aspect ratio and machine learning to measure tiredness in real time. KEYWORDS: Drowsiness detection, Eye aspect ratio, Machine learning, Eye tracking, Blink rate, Pupil dilation, Real-time monitoring, Non-invasive, Safety, Transportation