Estimating average growth trajectories in shape-space using kernel smoothing
Estimating average growth trajectories in shape-space using kernel smoothing
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DOI：
10.1109/tmi.2003.814784
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发表时间：
2003-06-01
影响因子：
10.6
通讯作者：
Potts, HWW
中科院分区：
文献类型：
--
作者：
Hutton, TJ;Buxton, BF;Potts, HWW
在本文中，我们展示了如何计算人脸的密集表面点分布模型，并展示了高维形状空间在表达与生长和衰老相关的形状变化方面的有用性。我们展示了如何在没有纵向数据的情况下，通过在人口中使用核平滑来计算人脸的平均生长轨迹。利用199名男性和201名年龄在0 - 50岁之间的女性受试者的三维表面扫描训练集来构建模型。
In this paper, we show how a dense surface point distribution model of the human face can be computed and demonstrate the usefulness of the high-dimensional shape-space for expressing the shape changes associated with growth and aging. We show how average growth trajectories for the human face can be computed in the absence of longitudinal data by using kernel smoothing across a population. A training set of three-dimensional surface scans of 199 male and 201 female subjects of between 0 and 50 years of age is used to build the model.