Drowsy driver detection 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 determine 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. These measures were
passed to learning-based classifiers such as Ad boost and multinomial ridge regression.
Head motion information was collected through automatic eye tracking and an Accelerometer.
The system was able to predict sleep and crash episodes on a
simulator with 98% accuracy across subjects. It is the highest prediction rate
reported to date for detecting drowsiness. Moreover, the analysis revealed
new information about human facial behavior for drowsy drivers.
Keywords Driver fatigue - Drowsiness - Machine learning - Facial expressions
- Facial action unit - Head movements - Multinomial logistic regression -
Support vector machine (SVM) - Coupling - Driver behavior
