ABSTRACT

In this paper, we propose a hypothesis that the facial landmark detection methods constructed by a private UPFP facial dataset can perform better than the model on a healthy facial dataset in the task of UPFP facial landmark detection. For proving this hypothesis, a customized UPFP facial dataset with 68 facial landmark annotations was built. A state-of-the-art facial landmark detection method was employed on the three evaluation datasets to exploit and prove the hypothesis. The mean error of validation dataset is 3.15, 56% lower than 7.42 that of the healthy dataset, which proves the hypothesis is true.

中文翻译：

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用于客观评估单侧周围性面瘫的面部成像和标志物检测技术

摘要

在本文中，我们提出了一个假设，即私有 UPFP 面部数据集构建的面部特征检测方法在 UPFP 面部特征检测任务中的性能优于健康面部数据集上的模型。为了证明这一假设，我们构建了一个定制的 UPFP 面部数据集，其中包含 68 个面部地标注释。在三个评估数据集上采用了最先进的面部标志检测方法来利用和证明该假设。验证数据集的平均误差为 3.15，比健康数据集的 7.42 低 56%，这证明了假设是正确的。