Machine Learning
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Head Pose Tracking With Stereovision
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Machine Learning
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Head Pose Estimation from Passive Stereo Images
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust real-time 3D head pose estimation from range data
Pattern Recognition
Efficient model-based linear head motion recovery from movies
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Head Pose Estimation Based on Random Forests for Multiclass Classification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
3D deformable face tracking with a commodity depth camera
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Regression forests for efficient anatomy detection and localization in CT studies
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Realtime performance-based facial animation
ACM SIGGRAPH 2011 papers
Real time head pose estimation from consumer depth cameras
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real time head pose estimation with random regression forests
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Supervised local subspace learning for continuous head pose estimation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Pattern Recognition Letters
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For real-time evaluation of the position and orientation of the human head using depth image, we propose a novel algorithm, the dynamic random regression forests (DRRF), which enhances the conventional random forests (RF) in four aspects. Firstly, the DRRF employs the boosting strategy for data induction to upgrade the learning quality; secondly, the key parameters are optimized in a dynamic manner in order to train the DRRF classifier efficiently; thirdly, a stem operator is integrated into the conventional tree-shaped classifier to increase the possibility of optimum data split; fourthly, a weighted voting scheme utilizes the learning knowledge to determine the regression result more efficiently and accurately. Comparative experiments verify the advantages of the aforementioned four improvement schemes, and demonstrate the DRRF's accuracy and robustness against partial occlusion and the variations of head pose, illumination, and facial expression.