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Kai Yuen, Simon Chong
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Real-time smart driver sleepiness detection by eye aspect ratio using computer vision Kai Yuen, Simon Chong; Zakaria, Noor Hidayah Binti; Eg Su, Goh; Hassan, Rohayanti; Kasim, Shahreen; Sutikno, Tole

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp677-686

Abstract

The purpose of this study is to determine the optimal eye aspect ratio (EAR) for a prototype capable of using computer vision techniques to detect driver sleepiness based on eyelid size changes. The prototype, developed with Raspberry Pi and OpenCV, provides a real-time evaluation of the driver's level of alertness. The prototype can accurately determine the onset of sleepiness by monitoring and detecting instances of prolonged eyelid closure. Due to the fact that the eye aspect ratios of different individuals vary in size, the system's accuracy may be compromised. For the first experiment, the research focuses on determining the optimal EAR threshold of the proposed prototype using a sample of 20 participants ranging in age from 20 to 30, 31 to 40, and 41 to 50 years old. The study also examines the effects of various environmental conditions, such as dark or nighttime settings and the use of spectacle. The optimal EAR threshold value, as dedicated by the first experiment, is 0.225 after testing 20 participants with and without eyeglasses in low and bright lighting and 7 participants with a 0.225 EAR threshold in dark and bright lighting environments. The result shows 100% precision.
Co-Authors Eg Su, Goh Rohayanti Hassan, Rohayanti Shahreen Kasim, Shahreen Tole Sutikno Zakaria, Noor Hidayah Binti