Eye Fatigue Detection in Vehicle Drivers Based on Facial Landmarks Features
Abstract
Human factors are unavoidable in traffic accidents, so early detection is needed. Fatigue while driving can be interpreted as drowsiness or nervous breakdown. Therefore, vehicle drivers must be able to increase their vigilance. The occurrence's cause is mainly human negligence due to tired driving. When the body feels tired, it is difficult to stay awake and focus. For this reason, this study was undertaken to propose a comprehensive approach to explore parameters indicating eye fatigue or driver drowsiness.. Detection of eye fatigue in vehicle drivers uses a smartphone camera (droid cam) to acquire and detect the driver's eyes using face recognition. This application development uses OpenCV with the Python programming language. This research was conducted by detecting faces using the facial landmarks 68 algorithms and then looking for points around the eyes to calculate the eye aspect ratio. The results of the detection analysis on the eyes when open and closed indicate the rider's size is tired and buggy. The input from the program is the number of blinks of the rider's eyes every minute so that the quantity indicates the driver is tired, sleepy or fit. This figure indicates that motorists are sleepy and need to anticipate taking a short break to continue the journey later.
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