Quantitative assessment and classification of facial paralysis (FP) are essential for treatment selection and progress evaluation of the condition. As part of a comprehensive framework towards this goal, this study aims to classify five normal facial functions: smiling, eye closure, raising the eyebrows, blowing cheeks, and whistling as well as the rest state. 3D facial landmarks and facial animation units (FAUs) were obtained using the Kinect V2, a fast and cost-effective depth camera. These were used to compute the features used in a Support Vector Machine (SVM) classifier. A dataset of 1650 records from 50 normal subjects was compiled for this study. The performances of different SVM kernel models were tested with different feature groups. The best performance (Accuracy=96.7%, Sensitivity=90.2%, and Specificity=98%) was found when using the RBF kernel model applied on just nine differences in FAUs. This research will be developed and extended to include FP classification.

面瘫 (FP) 的定量评估和分类对于治疗选择和病情进展评估至关重要。作为实现这一目标的综合框架的一部分，本研究旨在对五种正常的面部功能进行分类：微笑、闭眼、扬眉、吹脸、吹口哨以及休息状态。使用快速且经济高效的深度相机 Kinect V2 获得 3D 面部标志和面部动画单元 (FAU)。这些用于计算支持向量机 (SVM) 分类器中使用的特征。为这项研究编制了来自 50 名正常受试者的 1650 条记录的数据集。使用不同的特征组测试了不同 SVM 内核模型的性能。最佳性能（准确度=96.7%，灵敏度=90.2%，和特异性 = 98%）是在使用仅应用于 FAU 的九个差异的 RBF 内核模型时发现的。这项研究将得到发展和扩展，以包括 FP 分类。