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In an era where autonomous vehicles are on the horizon, the importance of human vigilance during driving cannot be understated. One of the paramount challenges road safety advocates face is driver fatigue, a silent culprit behind many tragic accidents. Our project seeks to address this issue by merging facial feature recognition with cutting-edge machine learning techniques, harnessing tools such as OpenCV and Dlib. This approach is centred around 68 precise facial feature detectors, adept at capturing specific markers like the status of a driver's eyes. Once data is acquired, our algorithms scrutinize it for fatigue indicators. Offering both cost and user benefits, our non-intrusive system swiftly alerts drivers, through auditory or tactile means, upon detecting drowsiness. Our system achieved a remarkable 94% efficiency in timely and accurate fatigue detection through exhaustive testing across varied scenarios, underscoring its potential to revolutionize road safety.
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Theivadas et al. (Sun,) studied this question.
www.synapsesocial.com/papers/68e6b7f0b6db643587638cc8 — DOI: https://doi.org/10.1016/j.measen.2024.101186
J Robert Theivadas
P. Suresh
Measurement Sensors
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
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