Abstract / truncated to 115 words (read the full abstract)

This thesis addresses the problem of drowsy driver detection using computer vision techniques applied to the human face. Specifically we explore the possibility of discriminating drowsy from alert video segments using facial expressions automatically extracted from video. Several approaches were previously proposed for the detection and prediction of drowsiness. There has recently been increasing interest in computer vision approaches as it is a potentially promising approach due to its non-invasive nature for detecting drowsiness. Previous studies with vision based approaches detect driver drowsiness primarily by making pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to explore, understand and exploit actual human behavior during drowsiness episodes. ... toggle 11 keywords

keywords: fatigue detection – driver drowsiness detection – computer vision – automatic facial expression recognition – machine learning – multinomial logistic regression – gabor filters – temporal analysis – iterative feature selection – facial action coding system (facs) – head motion

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