Eye State Classification Method for Detecting Physiological Deviations in Drivers Based on CNN Algorithm

This article presents an in-depth analysis of driver drowsiness detection using advanced deep learning techniques. We explore the application of Convolutional Neural Networks (CNNs) for classifying eye status (open vs. closed) as one of the key indicators of drowsiness, achieving a remarkable accuracy of 97% in comparison to traditional machine learning methods like Random Forest, Logistic Regression, and SVM. Additionally, we investigate the use of Recurrent Neural Networks (RNNs) for analyzing temporal dependencies in video data, focusing on yawning and head movement as supplementary indicators of driver fatigue. Despite the promising results, our research identifies limitations and challenges. Notably, variations in blink frequency and the nuanced relationship between blink patterns and drowsiness levels present complexities not fully addressed in this study. Future work will delve into these intricacies, aiming to develop more comprehensive and nuanced drowsiness detection systems capable of capturing subtle behavioral cues for enhanced accuracy and reliability.