Driver Drowsiness Detection Using Machine Learning and Image Processing

The number of automobile accidents due to driver drowsiness is increasing at an alarming rate. An automated non-contact system that can identify driver drowsiness early is the need of the hour. Motivated by this alarming need, a novel method is proposed that can detect driver drowsiness at an early stage and avoid mishaps. After the preprocessing of data using various features like Mouth Aspect Ratio, Eye Aspect Ratio, Pupil Circularity and Mouth Over Eye Ratio, the classification is done as comparative analysis of K-Nearest Neighbour, Naïve Bayes, Logistic Regression, Decision Trees, Random Forest, XGBoost, MLP and CNN on the University of Texas at Arlington Real-Life Drowsiness Dataset (UTA-RLDD) with different labels and thus we are able to detect the drowsiness. The accuracy was evaluated, and Logistic Regression provides the best accuracy in detecting drowsiness based on the dataset and was able to achieve 75.67% accuracy for 9080 samples.