1. Non-invasive Techniques for Driver Fatigue Monitoring
Qiang Ji
Dept. of Electrical, Computer, and Systems Engineering
Rensselaer Polytechnic Institute
Funded by AFOSR and Honda

3. Visual Behaviors Visual behaviors that typically reflect a
person's level of fatigue include
Eyelid movement
Head movement
Gaze
Facial expressions

4. Real time plot eyelid and eye gaze parameters over time
Real time plot eyelid and eye gaze parameters over time. AECS represents the average eye
closure and opening speed; PERCLOS is the percentage of eye closure; PERSAC is the percentage
of saccade eye movements over time.

6. Real time plot of face pose parameters (pan, tilt, and swing) and
facial expression parameter (mouth) over time. Face pose tracking
is to characterize head activity such as nodding and mouth movement
is used to detect mouth movement such as yawning.

7. The Dynamic Bayesian Network fatigue model for
modeling and detecting fatigue. It combines different
visual fatigue parameters with contextual information
(if available) to produce a composite fatigue score.

8. An overview of the fatigue monitor prototype
An overview of the fatigue monitor prototype. The prototype system: upper left corner shows the image
from the eye camera; upper right corner shows the image of face camera; bottom shows the real time
plot of the fatigue curve over time.

9. Dr. Qiang Ji of Rensselaer Polytechnic Institute in Troy, N.Y.,
                                                                                       
Stewart Cairns for The New York Times
Dr. Qiang Ji of Rensselaer Polytechnic Institute in Troy, N.Y., demonstrates a driver fatigue monitor.
Dr. Qiang Ji of Rensselaer Polytechnic Institute in Troy, N.Y.,
demonstrates a driver fatigue monitor.
From the business section of the New York Times Aug. 26,
2003.