Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Monitoring Head/Eye Motion for Driver Alertness with One Camera
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
A non-rigid motion estimation algorithm for yawn detection in human drivers
International Journal of Computational Vision and Robotics
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This paper proposes a robust and nonintrusive system for monitoring driver's fatigue and drowsiness in real time. The proposed scheme begins by extracting the face from the video frame using the Support Vector Machine (SVM) face detector. Then a new approach for eye and mouth state analysis -based on Circular Hough Transform (CHT)- is applied on eyes and mouth extracted regions. Our drowsiness analysis method aims to detect micro-sleep periods by identifying the iris using a novel method to characterize driver's eye state. Fatigue analysis method based on yawning detection is also very important to prevent the driver before drowsiness. In order to identify yawning, we detect wide open mouth using the same proposed method of eye state analysis. The system was tested with different sequences recorded in various conditions and with different subjects. Some experimental results about the performance of the system are presented.