A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Pose Estimation using 3D View-Based Eigenspaces
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Synergistic Face Detection and Pose Estimation with Energy-Based Models
The Journal of Machine Learning Research
Multi-View AAM Fitting and Construction
International Journal of Computer Vision
Multi-View AAM Fitting and Construction
International Journal of Computer Vision
Tensor Decompositions and Applications
SIAM Review
IEEE Transactions on Intelligent Transportation Systems
Head pose estimation using stereo vision for human-robot interaction
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Real time head pose estimation from consumer depth cameras
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Real time head pose estimation with random regression forests
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Tensor Learning for Regression
IEEE Transactions on Image Processing
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Real-time accurate head pose estimation is required for several applications. Methods based on 2D images might not provide accurate and robust head pose measurements due to large head pose variations and illumination changes. Robust and accurate head pose estimation can be achieved by integrating intensity and depth information. In this paper we introduce a head pose estimation system that employs random forests and tensor regression algorithms. The former allow the modeling of large head pose variations using large sets of training data, while the latter allow the estimation of more accurate head pose parameters. The combination of the above mentioned methods results in more robust and accurate predictions for large head pose variations. We also study the fusion of different sources of information (intensity and depth images) to determine how their combination affects the performance of a head pose estimation system. The efficiency of the proposed framework is tested on the Biwi Kinect Head Pose dataset, where it is shown that the proposed methodology outperforms typical random forests.