Drowsy driver detection through facial movement analysis

Authors:
Esra Vural;Mujdat Cetin;Aytul Ercil;Gwen Littlewort;Marian Bartlett;Javier Movellan
Affiliations:
Sabanci University, Faculty of Engineering and Natural Sciences, Orhanli, Istanbul and University of California San Diego, Institute of Neural Computation, La Jolla, San Diego;Sabanci University, Faculty of Engineering and Natural Sciences, Orhanli, Istanbul;Sabanci University, Faculty of Engineering and Natural Sciences, Orhanli, Istanbul;University of California San Diego, Institute of Neural Computation, La Jolla, San Diego;University of California San Diego, Institute of Neural Computation, La Jolla, San Diego;University of California San Diego, Institute of Neural Computation, La Jolla, San Diego
Venue:
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Year:
2007

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Abstract

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 preassumptions 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 computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy driving.