【摘 要】
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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 distributio
【机 构】
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National Engineering Research Center for E-Learning, Central China Normal University,Wuhan, China;Co
【出 处】
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第13届全国博士生学术年会——物联网专题
论文部分内容阅读
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 approachs robustness and efficiency.
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