Drowsy Driver Detection Through Facial Movement Analysis

The advance of computing technology has provided the means for building intelligent vehicle systems. Drowsy driver detection system is one of the potential applications of intelligent vehicle systems. Previous approaches to drowsiness detection primarily make pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving com...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Esra Vural, Müjdat Çetin, Aytül Erçil, Gwen Littlewort, Marian Stewart Bartlett, Javier R. Movellan
• Keywords: Computer Vision, Facial Actions, Human Behavior, ICCV 2007, Intelligent Vehicle Systems