International Journal of Computer Vision
Variance projection function and its application to eye detection for human face recognition
Pattern Recognition Letters
An analytic solution for the pose determination of human faces from a monocular image
Pattern Recognition Letters
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
An Algorithm for Real-Time Stereo Vision Implementation of Head Pose and Gaze Direction Measurement
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Determining driver visual attention with one camera
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents a framework that combines a robust facial features location with an elliptical face modelling to measure user's intention and 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.