期刊论文

Chen, Jingying

英文

International Journal of Pattern Recognition and Artificial Intelligence

0218-0014

2015

29

8

1556011

10.1142/S021800141556011X

Natural Science Fund of Hubei Province (2014CFB661), National Key Technology Research and Development Program (No. 2014BAH22F01), and Research Funds of CCNU from the Colleges Basic Research and Operation of MOE (No. CCNU14A05019). This work was supported by the National Key Technology Research and Development Program (No. 2013BAH18F02) and research funds from Ministry of Education and China Mobile (MCM20130601), Research funds from the Humanities and Social Sciences Foundation of the Ministry of Education (No. 14YJAZH005), Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE (CCNU13B001), Wuhan Chenguang Project (2013070104010019), Central China Normal University Research Start-up funding (No. 120005030223), the Scienti¯c Research Foundation for the Returned Overseas Chinese Scholars (No. (2013)693),

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Head pose and facial feature detection are important for face analysis. However, many studies reported good results in constrained environment, the performance could be decreased due to the high variations in facial appearance, poses, illumination, occlusion, expression and make-up. In this paper, we propose a hierarchical regression approach, Dirichlet-tree enhanced random forests (D-RF) for face analysis in unconstrained environment. D-RF introduces Dirichlet-tree probabilistic model into regression RF framework in the hierarchical way to achieve the efficiency and robustness. To eliminate n...