A drowsiness and point of attention monitoring system for driver vigilance

This paper presents a framework that combines a robust facial features location with an elliptical face modelling to measure driver's vigilance level. The proposed solution deals with the computation of eyelid movement parameters and head (face) point of attention. The most important facial feature points are automatically detected using a statistically anthropometric face model. After observing the structural symmetry of the human face and performing some anthropometric measurements, the system is able to build a model that can be used in isolating the most important facial feature areas: mouth, eyes and eyebrows. Combination of different image processing techniques are applied within the selected regions for detecting the most important facial feature points. A model based approach is used to estimate the 3D orientation of the human face. The shape of the face is modelled as an ellipse assuming that the human face aspect ratio (ratio of the major to minor axes of the 3D face ellipse) is known. The elliptical fitting of the face at the image level is constrained by the location of the eyes which considerable increase the performance of the system. The system is fully automatic and classifies rotation in all-view direction, detects eye blinking and eye closure and recovers the principal facial features points over a wide range of human head rotations. Experimental results using real images sequences demonstrates the accuracy and robustness of the proposed solution.