# Facial Paralysis Detection on Images Using Key Point Analysis
## 3 Experiments and Results  
There are a number of methodologies aiming to detect facial paralysis in a photograph. However, collaboration among the research community has been difficult due to the unavailability of public datasets, mainly because of patient privacy as many patients would prefer not to share their biometrical data. This situation motivated us to test our classification system on two different databases that recently became publicly available. Some remarks on such databases are given here. First, we used the Massachusetts Eye and Ear Infirmary (MEEI) database, which is an open source set of facial photographs and videos representing the entire spectrum of flaccid and nonflaccid facial palsy collected by [PERSON] et al. and introduced in [19]. The MEEI database was released to serve as a resource for facial palsy education and research. Initially, to demonstrate the utility of the database the relationship between the level of facial function and the perceived emotion expression was successfully characterized using a machine learning-based algorithm [19]. Later, the MEEI database was employed to develop a novel machine learning algorithm for the fast and accurate localization of facial landmarks in photographs of facial palsy patients; the improved shape predictor represents the first step toward an automatic system for computer-aided assessment in facial palsy [12]. In order to determine an agreement between the facial function evaluation using high-quality photographs and using in-person evaluation, the MEEI database was employed. The authors demonstrated that facial symmetry in facial palsy patients can be monitored using standardized frontal photographs [20]. Recently, the MEEI database was also employed to compare a clinician evaluation against machine learning-derived automated assessments in frontal photographs. The authors of [21] concluded that automated scores predicted more asymmetry in normal patients and less asymmetry in patients with flaccid palsy and synthesis compared to clinician grading. Automated assessments hold promise for the standardization of facial palsy outcome measures and may eliminate the observer bias seen in clinician-graded scales. The database is composed of 480 high-resolution images from 60 participants, 10 healthy subjects, and 50 patients (25 suffering from flaccid and 25 from nonflaccid paralysis), each one performing 8 different facial movements: (1) at rest, (2) eyebrow elevation, (3) light effort eye closure, (4) full effort closure, (5) light effort smile, (6) full effort smile, (7) pucker, and (8) lip depression. This image database was used to design our classification system. To the best of our knowledge, there is no other publicly available database with these characteristics. Second, the Toronto NeuroFace (TNF) is also a publicly available dataset, collected by [PERSON] et al. and introduced in [22], which aims to assess neurological disorders. Similarly to the MEEI dataset, the Toronto NeuroFace dataset was released to foster the development of novel and robust approaches for face alignment and oro-facial assessments that can be used to track and analyze facial movements in clinical populations suffering from amyotrophic lateral sclerosis and other neurological diseases. Authors in [22] analyzed the importance of using algorithms trained with data from the target population in order to improve the localization of facial landmarks and also the accuracy in face alignment. The TNF dataset consists of 261 videos, clinical scores per video, and more than 3300 annotated frames of faces from individuals performing oro-facial tasks typical of the clinical assessment. To the best of our knowledge, neither the MEEI database or the TNF dataset have been employed to specifically detect facial paralysis in frontal face photography.