Efficient Driver Drowsiness Detection Using Spatiotemporal Features with Support Vector Machine

Drowsiness significantly impairs human concentration and reflexes, leading to a heightened risk of accidents. Despite this, many drivers fail to recognize their drowsiness in time, often with serious consequences. Traditional detection systems based on vehicle movement and steering angles are inadequate in preventing such incidents. Existing vision-based systems, while promising, are typically limited to eye movement analysis, require extensive parameter tuning, and often struggle under varying conditions. To address these challenges, we propose a novel approach for Driver Drowsiness Detection that leverages facial features. Our method utilizes Local Binary Patterns on Three Orthogonal Planes for feature extraction and employs Support Vector Machines for classification. Experiments conducted on two benchmark drowsiness datasets, UTA-RLDD and DROZY, demonstrate our system's efficacy, achieving accuracy rates of 82%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$82\%$$\end{document} and 90%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$90\%$$\end{document}, respectively. These results indicate the potential for a more reliable and non-invasive drowsiness detection system.