Assessment of facial paralysis (FP) and quantitative grading of facial asymmetry are essential in order to quantify the extent of the condition as well as to follow its improvement or progression. As such, there is a need for an accurate quantitative grading system that is easy to use, inexpensive and has minimal inter-observer variability. A comprehensive automated system to quantify and grade FP is the main objective of this work. An initial prototype has been presented by the authors. The present research aims to enhance the accuracy and robustness of one of this system's modules: the resting symmetry module. This is achieved by including several modifications to the computation method of the symmetry index (SI) for the eyebrows, eyes and mouth. These modifications are the gamma correction technique, the area of the eyes, and the slope of the mouth. The system was tested on normal subjects and showed promising results. The mean SI of the eyebrows decreased slightly from 98.42% to 98.04% using the modified method while the mean SI for the eyes and mouth increased from 96.93% to 99.63% and from 95.6% to 98.11% respectively while using the modified method. The system is easy to use, inexpensive, automated and fast, has no inter-observer variability and is thus well suited for clinical use.

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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.

Quantitative grading of facial paralysis (FP) and the associated loss of facial function are essential to evaluate the severity and to track deterioration or improvement of the condition following treatment. To date, several computer-assisted grading systems have been proposed but none have gained widespread clinical acceptance. There is still a need for an accurate quantitative assessment tool that is automatic, inexpensive, easy to use, and has low inter-observer variability. The aim of the authors is to develop such a comprehensive Automated Facial Grading (AFG) system. One of this system's modules: the resting symmetry module has already been presented. The present study describes the implementation of the second module for grading voluntary movements. The system utilizes the Kinect v2 sensor to detect and capture facial landmarks in real time. The functions of three regions, the eyebrows, eyes and mouth, are evaluated by quantitatively grading four voluntary movements. Preliminary results on normal subjects and patients are promising. The AFG system is a novel system that is suitable for clinical use because it is fast, objective, easy to use, and inexpensive. With further enhancement, it can be extended to become a virtual facial rehabilitation tool.

Facial paralysis (FP) is an inability to move facial muscles voluntarily, affecting daily activities. There is a need for quantitative assessment and severity level classification of FP to evaluate the condition. None of the available tools are widely accepted. A comprehensive FP evaluation system has been developed by the authors. The system extracts real-time facial animation units (FAUs) using the Kinect V2 sensor and includes both FP assessment and classification. This paper describes the development and testing of the FP classification phase. A dataset of 375 records from 13 unilateral FP patients and 1650 records from 50 control subjects was compiled. Artificial Intelligence and Machine Learning methods are used to classify seven FP categories: the normal case and three severity levels: mild, moderate, and severe for the left and right sides. For better prediction results (Accuracy = 96.8%, Sensitivity = 88.9% and Specificity = 99%), an ensemble learning classifier was developed rather than one weak classifier. The ensemble approach based on SVMs was proposed for the high-dimensional data to gather the advantages of stacking and bagging. To address the problem of an imbalanced dataset, a hybrid strategy combining three separate techniques was used. Model robustness and stability was evaluated using fivefold cross-validation. The results showed that the classifier is robust, stable and performs well for different train and test samples. The study demonstrates that FAUs acquired by the Kinect sensor can be used in classifying FP. The developed FP assessment and classification system provides a detailed quantitative report and has significant advantages over existing grading scales.