Automatic Assessment of Facial Paralysis Based on Facial Landmarks

Unilateral peripheral facial paralysis is the most common case of facial paralysis. It affects only one side of the face, which will cause facial asymmetry. Clinically, unilateral peripheral facial paralysis is often classified by clinicians according to evaluation scales, based on patients’ condition of facial symmetry. A prevalent scale is House-Brackmann grading system (HBGS). However, assessment results from scales are often with great subjectivity, and will bring high interobserver and intraobserver variability. Therefore, this manuscript proposed an objective method to provide assessment results by using facial videos and applying machine learning models. This grading method is based on HBGS, but it is automatically implemented with high objectivity. Images with facial expressions will be extracted from the videos to be analyzed by a machine learning model. Facial landmarks will be acquired from the images by using a 68-points model provided by dlib. Then index and coordinate information of the landmarks will be used to calculate the values of features pre-designed to train the model and predict the result of new patients. Due to the difficulty of collecting facial paralysis samples, the data size is limited. Random Forest (RF) and support vector machine (SVM) were compared as the classifiers. This method was applied on a data set of 33 subjects. The highest overall accuracy rate reached 88.9%, confirming the effectiveness of this method.