Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
An analytic solution for the pose determination of human faces from a monocular image
Pattern Recognition Letters
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Active Face Tracking and Pose Estimation in an Interactive Room
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Head Pose Determination from One Image Using a Generic Model
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
3D Head Pose Estimation without Feature Tracking
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
3D head pose computation from 2D images: templates versus features
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Model-Based Head Pose Tracking With Stereovision
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Full-Motion Recovery of Head by Dynamic Templates and Re-Registration Techniques
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gaze direction detection from thermal camera image
IMMURO'12 Proceedings of the 11th WSEAS international conference on Instrumentation, Measurement, Circuits and Systems, and Proceedings of the 12th WSEAS international conference on Robotics, Control and Manufacturing Technology, and Proceedings of the 12th WSEAS international conference on Multimedia Systems & Signal Processing
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This paper proposes a method of determining human head poses from a sequence of images. The main idea is to use some features in a 3D head model to generate a virtual fronto-parallel projection that satisfies conditions of affine approximation. Then the affine parameters between the virtual projection and input view are calculated. After that, rotation and translation parameters of the head are roughly estimated by a circle-ellipse correspondence technique based on the affine parameters. Finally, an iterative optimization algorithm is utilized further to refine the results. The accuracy is maintained by estimating reliability of the 2D-3D feature correspondences and weighting each factor of the optimization objective function. The system performance is also improved by applying a modified KLT technique to speed up the convergence during the face feature tracking process. Experimental results show that our method can accurately recover head poses in a wide range of head motion.