@inproceedings{e97bf6eee4064d728427197a44430a18,
title = "A Combined Generalized and Subject-Specific 3D Head Pose Estimation",
abstract = "We propose a real-time method for 3D head pose estimation from RGB-D sequences. Our algorithm relies on a Random Forest framework that is able to regress the head pose at every frame in a temporal tracking manner. Such framework is learned once from a generic dataset of 3D head models and refined online to adapt the forest to the specific characteristics of each subject. Through the qualitative experiments under different conditions, it demonstrates remarkable properties in terms of robustness to occlusions, computational efficiency and capacity of handling a variety of challenging head poses. In addition, it also outperforms the state of the art on the reference benchmark dataset with regards to the accuracy of the estimated head poses.",
keywords = "Accuracy, Cameras, Head, Robustness, Solid modeling, Three-dimensional displays",
author = "Tan, {David Joseph} and Federico Tombari and Nassir Navab",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 International Conference on 3D Vision, 3DV 2015 ; Conference date: 19-10-2015 Through 22-10-2015",
year = "2015",
month = nov,
day = "20",
doi = "10.1109/3DV.2015.62",
language = "English",
series = "Proceedings - 2015 International Conference on 3D Vision, 3DV 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "500--508",
editor = "Michael Brown and Jana Kosecka and Christian Theobalt",
booktitle = "Proceedings - 2015 International Conference on 3D Vision, 3DV 2015",
}