The use of pseudo-landmarks for craniofacial analysis: a comparative study with L₁-regularized logistic regression.
The use of pseudo-landmarks for craniofacial analysis: a comparative study with L₁-regularized logistic regression.
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DOI：
10.1109/embc.2013.6610940
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Lee SI
中科院分区：
文献类型：
--
作者：
Mercan E;Shapiro LG;Weinberg SM;Lee SI
形态测量学是一种定量分析面部形状的方法，被颅面研究者用来研究人类面部形状的异常。颅面形态测量中的大部分工作使用在3D面部数据上手动标记并通过广义Procrustes分析处理的标志点。对于大型数据集，此手动过程非常耗时。密集的伪地标集也被提出并成功地用于分类和聚类，但文献中的两种主要方法都是计算密集型的。我们已经开发了一种计算简单的方法，可以计算不同分辨率的伪地标点从三维网格的人脸。在本文中，我们进行了一项比较研究，采用L1正则化逻辑回归训练分类器，预测500正常成人面部网格的性别，以比较我们的方法，两个替代的伪地标方法和距离矩阵方法。我们的研究结果表明，我们的方法，这是完全自动的，取得了类似的结果，最好的评分方法，没有手动标记和更低的计算时间。使用距离矩阵并没有改善分类结果。
Morphometrics, the quantitative analysis of shape, is used by craniofacial researchers to study abnormalities in human face shapes. Most of the work in craniofacial morphometrics uses landmark points that are manually marked on 3D face data and processed via a generalized Procrustes analysis. For large data sets this manual process is very time-consuming. Dense sets of pseudo-landmarks have also been proposed and successfully used for classification and clustering, but the two main methods in the literature are very computationally intensive. We have developed a computationally simple method that can compute pseudo-landmark points at different resolutions from 3D meshes of human faces. In this paper, we perform a comparative study employing L1-regularized logistic regression to train a classifier that predicts the sex of 500 normal adult face meshes in order to compare our method to two alternative pseudo-landmark methods and a distance matrix approach. Our results show that our method, which is fully automatic, achieved similar results to the best-scoring methods with no manual landmarking and with much lower computation time. Use of the distance matrix did not improve classification results.