Camera-based Driver Drowsiness State Classification Using Logistic Regression Models

Drowsiness at the wheel is a major problem for traffic road safety. A drowsy driver suffers from decreased vigilance, increased reaction time and degraded decision-making ability, all of which have a huge impact on the driving performance. A driver monitoring system that warns the driver of his or her critical drowsiness state is a worthwhile contribution to traffic road safety. A drowsy driver typically exhibits some observable behaviors, such as eye blinking and head movements, that can be tracked using a camera. In this study, we analyze the potential of eye closure and head rotation signals, provided by a driver camera, to classify the driver's drowsiness state using logistic regression models. This analysis is based on a large dataset collected from 71 subjects in driving simulator experiments. A reliable and independent reference for drowsiness, however, is required in order to perform this analysis. For this purpose, we devise a methodology that merges several drowsiness monitoring approaches to construct a reliable reference for drowsiness. Furthermore, we describe our approach to extract eye blink and head rotation features. Ultimately, we design logistic regression classifiers and combine them using the one-vs-one binarization technique. Our approach achieves a global balanced validation accuracy of 72.7% on a three-class classification problem (awake, questionable and drowsy) by adopting a strict and rigorous evaluation scheme (i.e., leave-one-drive-out cross-validation).