Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks.

Researchers

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Abstract

This study aimed to demonstrate the application of our automated facial recognition system to measure facial nerve function and compare its effectiveness with other conventional systems and provide a preliminary evaluation of deep learning-facial grading systems.
Retrospective, observational.
Tertiary referral center, hospital.
Facial photos taken from 128 patients with facial paralysis and two persons with no history of facial palsy were analyzed.
Diagnostic.
Correlation with Sunnybrook (SB) and House-Brackmann (HB) grading scales.
Our results had good reliability and correlation with other grading systems (r = 0.905 and 0.783 for Sunnybrook and HB grading scales, respectively), while being less time-consuming than Sunnybrook grading scale.
Our objective method shows good correlation with both Sunnybrook and HB grading systems. Furthermore, this system could be developed into an application for use with a variety of electronic devices, including smartphones and tablets.