Driver Drowsiness Prediction Based on Multiple Aspects Using Image Processing Techniques

The majority of the accidents were happening perpetually due to driver drowsiness over the decades. Automation has been playing key role in many fields to provide conformity and improve the quality of life of the users. Though various drowsiness detection systems have been developed during last decade based on many factors, still the systems were demanding an improvement in terms of efficiency, accuracy, cost, speed, and availability, etc. In this paper, proposed an integrated approach depends on the Eye and mouth closure status (PERCLOS) along with the calculation of the new proposed vector FAR (Facial Aspect Ratio) similarly to EAR and MAR. This helps to find the status of the closed eyes or opened mouth like yawning, and any frame finds that has hand gestures like nodding or covering opened mouth with hand as innate nature of humans when trying to control the sleepiness. The system also integrated the methods and textural-based gradient patterns to find the driver’s face in various directions identify the sunglasses on the driver’s face and the scenarios like hands-on eyes or mouth while nodding or yawning were also recognized and addressed. The proposed work tested on datasets such as NTHU-DDD, YawDD, and a proposed dataset EMOCDS (Eye and Mouth Open Close Data Set) and proved better in terms of accuracy and provides results in general by considering various circumstances.