In this study, we propose to diagnose facial nerve palsy using Support Vector Machines
(SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial
palsy domain using facial features and grade the degree of nerve damage based on
the House-Brackmann score. Traditional diagnostic approaches involve a medical doctor
recording a thorough history of a patient and determining the onset of paralysis, rate
of progression and so on. The most important step is to assess the degree of voluntary
movement of the facial nerves and document the grade of facial paralysis using House-
Brackmann score. The significance of the work is the attempt to understand the diagnosis
and grading processes using semi-supervised learning with the aim of automating the
process. The value of the research is in identifying and documenting the limited literature
seen in this area. The use of automated diagnosis and grading greatly reduces the
duration of medical examination and increases the consistency, because many palsy images
are stored to provide benchmark references for comparative purposes. The proposed
automated diagnosis and grading are computationally efficient. This automated process
makes it ideal for remote diagnosis and examination of facial palsy. The profiling of a
large number of facial images are captured using mobile phones and digital cameras.
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