Driver fatigue detection method based on facial image analysis

Nowadays, ensuring road safety is a crucial issue that demands continuous development and measures to minimize the risk of accidents. This paper presents the development of a driver fatigue detection method based on the analysis of facial images. To monitor the driver's condition in real-time, a video camera was used. The method of detection is based on analyzing facial features related to the mouth area and eyes, such as the frequency of blinking and yawning, mouth aspect ratio (MAR), and the duration of eye closure. The method was implemented in Python using a convolutional neural network (CNN). To validate the method, a dataset was created containing eye images that were subjected to various modifications, including the use of corrective glasses. The model's results confirm the method's effectiveness in detecting fatigue, achieving an average accuracy of 92% for eye detection and 82% for yawning detection under well-lit conditions.