Graph embedded analysis for head pose estimation

Yun Fu, Thomas S. Huang
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引用次数: 127

Abstract

Head pose is an important vision cue for scene interpretation and human computer interaction. To determine the head pose, one may consider the low-dimensional manifold structure of the face view points in image space. In this paper, we present an appearance-based strategy for head pose estimation using supervised graph embedding (GE) analysis. Thinking globally and fitting locally, we first construct the neighborhood weighted graph in the sense of supervised LLE. The unified projection is calculated in a closed-form solution based on the GE linearization. We then project new data (face view images) into the embedded low-dimensional subspace with the identical projection. The head pose is finally estimated by the K-nearest neighbor classification. We test the proposed method on 18,100 USF face view images. Experimental results show that, even using a very small training set (e.g. 10 subjects), GE achieves higher head pose estimation accuracy with more efficient dimensionality reduction than the existing methods
头部姿态估计的图嵌入分析
头部姿态是场景解释和人机交互的重要视觉线索。为了确定头部姿态,可以考虑图像空间中面部视点的低维流形结构。在本文中,我们提出了一种基于外观的头部姿态估计策略,该策略使用监督图嵌入(GE)分析。从全局和局部拟合的角度出发,首先构造有监督LLE意义上的邻域加权图。统一投影的计算是基于GE线性化的封闭解。然后,我们用相同的投影将新数据(面部视图图像)投影到嵌入的低维子空间中。最后通过k近邻分类估计头部姿态。我们对18100张USF人脸图像进行了测试。实验结果表明,即使使用非常小的训练集(例如10个受试者),GE也能比现有方法获得更高的头姿估计精度和更有效的降维
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