Robust Driver Drowsiness Detection via CNN–SVM Ensemble Learning

Abstract

Driver drowsiness is one of the leading causes of road accidents, highlighting the need for accurate and real-time monitoring systems to improve road safety. This paper presents an intelligent driver drowsiness detection framework that combines computer vision and machine learning techniques to classify driver states as alert, drowsy, or sleepy. The proposed system continuously captures facial images using a frontal camera and analyzes behavioral indicators such as Eye Aspect Ratio (EAR) and Mouth Opening Ratio (MOR) through OpenCV and Dlib. Deep features extracted from facial images are processed using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), while a weighted ensemble fusion strategy integrates the predictions of both models to enhance classification accuracy and robustness. To improve detection reliability, an additional low-angle camera is employed to monitor the driver’s face when head movements or downward postures reduce the visibility of the frontal camera. Experimental results demonstrate that the proposed CNN–SVM fusion model provides accurate and reliable driver state classification, making it suitable for real-time driver monitoring applications. The proposed framework offers a practical and scalable solution for reducing fatigue-related road accidents and improving intelligent transportation safety.