# Facial Paralysis Detection on Images Using Key Point Analysis  
[PERSON]  
1 Department of Electronics Engineering, Universidad de Guanajuato DICIS, Salamanca 36885, Mexico;  
[EMAIL_ADDRESS] (G.S.P.-D.); [EMAIL_ADDRESS] (C.H.G.-C.)  
1  
[PERSON]  
1 Department of Electronics Engineering, Universidad de Guanajuato DICIS, Salamanca 36885, Mexico;  
[EMAIL_ADDRESS] (G.S.P.-D.); [EMAIL_ADDRESS] (C.H.G.-C.)  
1  
[PERSON]  
1 Department of Electronics Engineering, Universidad de Guanajuato DICIS, Salamanca 36885, Mexico;  
[EMAIL_ADDRESS] (G.S.P.-D.); [EMAIL_ADDRESS] (C.H.G.-C.)  
1  
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facial landmarks) based on a specific model. There are some publicly available models, also known as shape predictors, that extract facial landmarks using Haar cascades. The 68 points predictor proposed by [PERSON] and [PERSON] in [PERSON] and [PERSON] (1986) is widely known and employed in the field of facial analysis. In the literature, some authors create their own shape predictor to extract facial landmarks with a better performance of the task [4]. In a common approach, the extraction of facial landmarks is a crucial step, because these key points are used to compute other measures, such as the distances, angles, and areas between the landmarks. In the facial analysis process, those measures are fed into a classifier to train it and to detect facial nerve damage, and, later, its severity. To achieve the automatic classification of facial nerve damage, it is necessary for a clinical practitioner to evaluate a set of facial images to label them as healthy or unhealthy faces. With annotated data, any classifier could be trained using the extracted facial measures and the labels to detect facial nerve damage in a new image. Although a variety of automated facial nerve function assessment solutions have been proposed, their outcomes fall into two main categories: (1) non-semantic numerical values quantifying static, dynamic, and synthetic facial features; (2) semantic grade of facial nerve function designed by the clinician [5]. Most existing solutions belong to the first category; however, most of these solutions stay at the method discussion phase, and only a few of them have been implemented into prototypes--for example, the _Enotrics_ software [6]. Solutions in the second category aim to quantify the facial nerve function according to a specific facial nerve grading scale designed by clinicians. To achieve this target, machine learning techniques should be applied to build a predictive model which is trained on labelled data and capable of making predictions on new data. Here, the data are facial images from either a healthy subject or a facial palsy patient, and the prediction is a binary decision (healthy or patient) or it could be the grade of facial nerve function. Classifiers for these applications are based on the methods of support vector machine (SVM), artificial neural network (ANN), k-nearest-neighbor (KNN), or hybrid classifier [5]. For a new subject, a typical system extracts computational features from the facial data then calls a pre-trained classifier to map the features to the facial nerve malfunction. There are some works that use facial landmarks (i.e., facial key points extraction), before performing facial analysis [6; 7; 8]. There are other works that specifically intend to detect facial paralysis as a binary classification problem. [PERSON] et al. proposed a smartphone-based automatic diagnosis system that consists of three modules: facial landmark detector, feature extractor based on facial regions, and a classifier [9]. Three facial movements were analyzed: resting, smiling, and raising the eyebrows. The system was evaluated on a private database with 23 facial palsy patients and 13 healthy volunteers.