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Authors: Yuanyuan Liu 1 ; Jingying Chen 2 ; Leyuan Liu 2 ; Yujiao Gong 3 and Nan Luo 3

Affiliations: 1 Central China Normal University, Collaborative & Innovative Center for Educational Technology (CICET) and Huazhong University of Science and Technology Wenhua College, China ; 2 Central China Normal University and Collaborative & Innovative Center for Educational Technology (CICET), China ; 3 Central China Normal University, China

ISBN: 978-989-758-018-5

Keyword(s): Dirichlet-tree distribution enhanced random forests. Head pose estimation. Gaussion mixture model. Positive patch extraction.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Gaussian Processes ; Geometry and Modeling ; ICA, PCA, CCA and other Linear Models ; Image-Based Modeling ; Incremental Learning ; Learning and Adaptive Control ; Multiclassifier Fusion ; Object Recognition ; Pattern Recognition ; Software Engineering ; Theory and Methods

Abstract: Head pose estimation is important in human-machine interfaces. However, illumination variation, occlusion and low image resolution make the estimation task difficult. Hence, a Dirichlet-tree distribution enhanced Random Forests approach (D-RF) is proposed in this paper to estimate head pose efficiently and robustly under various conditions. First, Gabor features of the facial positive patches are extracted to eliminate the influence of occlusion and noise. Then, the D-RF is proposed to estimate the head pose in a coarse-to-fine way. In order to improve the discrimination capability of the approach, an adaptive Gaussian mixture model is introduced in the tree distribution. The proposed method has been evaluated with different data sets spanning from -90º to 90º in vertical and horizontal directions under various conditions.The experimental results demonstrate the approach’s robustness and efficiency.

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Paper citation in several formats:
Liu Y., Chen J., Liu L., Gong Y. and Luo N. (2014). Dirichlet-tree Distribution Enhanced Random Forests for Head Pose Estimation.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 87-95. DOI: 10.5220/0004825000870095

@conference{icpram14,
author={Yuanyuan Liu and Jingying Chen and Leyuan Liu and Yujiao Gong and Nan Luo},
title={Dirichlet-tree Distribution Enhanced Random Forests for Head Pose Estimation},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={87-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004825000870095},
isbn={978-989-758-018-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Dirichlet-tree Distribution Enhanced Random Forests for Head Pose Estimation
SN - 978-989-758-018-5
AU - Liu Y.
AU - Chen J.
AU - Liu L.
AU - Gong Y.
AU - Luo N.
PY - 2014
SP - 87
EP - 95
DO - 10.5220/0004825000870095

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