Dirichlet-tree Distribution Enhanced Random Forests for Head Pose Estimation
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 featuresand histogram distributions of the facial 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-90° to 90° in vertical and horizontal directions under various conditions.The experimental results demonstrate the approach”s robustness and efficiency.
Positive patches extraction Dirichlet-tree distribution enhanced random forests Head pose estimation Adaptive Gaussian mixture model
Yuanyuan Liu Jingying Chen Leyuan Liu Yujiao Gong Nan Luo
National Engineering Research Center for E-Learning, Central China Normal University,Wuhan, China;Co National Engineering Research Center for E-Learning, Central China Normal University,Wuhan, China
国内会议
广州
英文
317-326
2015-05-01（万方平台首次上网日期，不代表论文的发表时间）