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Traffic accidents caused by driver drowsiness are a leading factor in fatal road incidents. This study introduces a computer vision-based drowsiness detection system utilizing two methods: Eye Aspect Ratio (EAR) and Iris-Sclera Pattern Analysis (ISPA). The EAR method measures the eye aspect ratio to determine whether the eyes are open or closed. This involves calculating the vertical distance between specific landmark points on the eyelids and comparing it to the horizontal distance between points on the eye. A decrease in this ratio serves as an early indicator of drowsiness. The ISPA method employs symmetry analysis between the iris and sclera. This approach relies on the visual pattern formed when the eyes are open, where the sclera appears symmetrically distributed around the iris. During this process, eye images are processed to extract iris and sclera features, which are then analyzed for symmetry to detect signs of drowsiness. The study evaluates the reliability of both methods under varying conditions, such as changes in lighting, viewing distances, head movements, and the use of eyeglasses. The results show that the EAR method achieved an accuracy of 83.33% in distance testing, indicating its effectiveness in stable lighting environments. In contrast, the ISPA method achieved an accuracy of 59.25% under low and variable lighting conditions and proved more reliable for detecting the eyes of users wearing glasses.
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