SVM classification of facial functions based on facial landmarks and animation Units

Abstract

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.