Abstract
Driver drowsiness remains a critical factor in road safety, necessitating the development of robust detection methodologies. This study presents a dual-framework approach that integrates a convolutional neural network (CNN) and a facial landmark analysis model to enhance drowsiness detection. The CNN model classifies driver states into "Awake" and "Drowsy", achieving a classification accuracy of 92.5%. In parallel, a deep learning-based facial landmark analysis model analyzes a driver's physiological state by extracting and analyzing facial features. The model's accuracy was significantly enhanced through advanced image preprocessing techniques, including image normalization, illumination correction, and face hallucination, reaching a 97.33% classification accuracy. The proposed dual-model architecture leverages imagery analysis to detect key drowsiness indicators, such as eye closure dynamics, yawning patterns, and head movement trajectories. By integrating CNN-based classification with precise facial landmark analysis, this study not only improves detection robustness but also ensures greater resilience under challenging conditions, such as low-light environments. The findings underscore the efficacy of multi-model approaches in drowsiness detection and their potential for real-world implementation to enhance road safety and mitigate drowsiness-related vehicular accidents.