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Robust head pose estimation using Dirichlet-tree distribution enhanced random forests

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成果类型:
期刊论文
作者:
Liu, Yuanyuan;Chen, Jingying*;Su, Zhiming;Luo, Zhenzhen;Luo, Nan;...
通讯作者:
Chen, Jingying
作者机构:
[Liu, Leyuan; Luo, Zhenzhen; Su, Zhiming; Luo, Nan; Zhang, Kun; Liu, Yuanyuan; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
[Liu, Yuanyuan] Wenhua Coll, Wuhan, Peoples R China.
通讯机构:
[Chen, Jingying] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
语种:
英文
关键词:
Database systems;Decision trees;Forestry;Image resolution;Textures;Attention estimations;Geometric feature;Head Pose Estimation;Human Machine Interface;Probabilistic models;Unconstrained environments;Weighted voting;Weighted voting methods;Random forests;accuracy;Article;classification;classification algorithm;Dirichlet tree distribution enhanced random forest;facies;image analysis;intermethod comparison;mathematical analysis;mathematical model;noise;prediction;priority journal;probability;random forest
期刊:
Neurocomputing
ISSN:
0925-2312
年:
2016
卷:
173
期:
P1
页码:
42-53
基金类别:
This work was supported by the National Key Technology Research and Development Program (no. 2013BAH18F02 ), Research funds from Ministry of Education and China Mobile ( MCM20130601 ), Research Funds of CCNU from the Colleges׳ Basic Research and Operation of MOE ( CCNU13B001 ), Research funds from the Humanities and Social Sciences Foundation of the Ministry of Education (no. 14YJAZH005 ), Central China Normal University Research Start-up funding (no. 120005030223 ), The Scientific Research Foundation for the Returned Overseas Chinese Scholars (no. (2013)693 ), Research Funds of CCNU from the Colleges׳ Basic Research and Operation of MOE (no. CCNU14A05020 , no. CCNU14A05019 ), National Natural Science Foundation of China (no. 61272206 ), National Key Technology Research and Development Program (no. 2014BAH22F01 ).
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Head pose estimation (HPE) is important in human-machine interfaces. However, various illumination, occlusion, low image resolution and wide scene 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 in unconstrained environment. First, positive/negative facial patch is classified to eliminate influence of noise and occlusion. Then, the D-RF is proposed to estimate the head pose in a coarse-to-fine way using more powerful combined texture and geometric features o...

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