论文标题
对3D面对准的无监督性能分析，并具有统计上强大的置信度测试
Unsupervised Performance Analysis of 3D Face Alignment with a Statistically Robust Confidence Test
论文作者
论文摘要
本文解决了分析3D面对准（3DFA）或面部地标本地化的性能的问题。通常根据注释的数据集对此任务进行监督。然而，在3DFA的特殊情况下，注释过程很少没有错误，这极大地偏向了结果。另外，研究了无监督的绩效分析（UPA）。所提出的方法的核心成分是对预测地标和模型地标之间的刚性转换的强大估计。结果表明，如此计算的刚性映射由于表达和身份的变异性而或地标性定位误差，由于各种扰动而导致非刚性面部变形。指导的想法是将估计的旋转，翻译和比例应用于一组预测的地标，以便将它们映射到数学房屋上，以将这些地标嵌入的形状（包括可能的错误）映射到数学上。 UPA收益如下：（i）使用正在研究的3DFA方法从2D面中提取3D地标； （ii）这些地标被严格映射到规范（额叶）姿势上，（iii）为每个地标计算统计上固定的置信度评分。这允许评估映射的地标是在内部（嵌入式）还是外部（异常值）置信量。详细描述了一种实验评估协议，该协议使用公开可用的数据集和几个与已发表文章相关的3DFA软件包。结果表明，提出的分析与监督指标一致，并且可以用来测量预测地标和自动注释的3DFA数据集的准确性，以检测错误并消除它们。本文的源代码和补充材料可在https://team.inria.fr/robotlearn/upa3dfa/上公开获得。
This paper addresses the problem of analysing the performance of 3D face alignment (3DFA), or facial landmark localization. This task is usually supervised, based on annotated datasets. Nevertheless, in the particular case of 3DFA, the annotation process is rarely error-free, which strongly biases the results. Alternatively, unsupervised performance analysis (UPA) is investigated. The core ingredient of the proposed methodology is the robust estimation of the rigid transformation between predicted landmarks and model landmarks. It is shown that the rigid mapping thus computed is affected neither by non-rigid facial deformations, due to variabilities in expression and in identity, nor by landmark localization errors, due to various perturbations. The guiding idea is to apply the estimated rotation, translation and scale to a set of predicted landmarks in order to map them onto a mathematical home for the shape embedded in these landmarks (including possible errors). UPA proceeds as follows: (i) 3D landmarks are extracted from a 2D face using the 3DFA method under investigation; (ii) these landmarks are rigidly mapped onto a canonical (frontal) pose, and (iii) a statistically-robust confidence score is computed for each landmark. This allows to assess whether the mapped landmarks lie inside (inliers) or outside (outliers) a confidence volume. An experimental evaluation protocol, that uses publicly available datasets and several 3DFA software packages associated with published articles, is described in detail. The results show that the proposed analysis is consistent with supervised metrics and that it can be used to measure the accuracy of both predicted landmarks and of automatically annotated 3DFA datasets, to detect errors and to eliminate them. Source code and supplemental materials for this paper are publicly available at https://team.inria.fr/robotlearn/upa3dfa/.
