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Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks
Se A Lee1, Jin Kim2, Jeon Mi Lee2
1Department of Otorhinolaryngology-Head and Neck Surgery, Yonsei University College of Medicine, Seoul.
Objectives:
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.
Study Design:
Retrospective, observational.
Setting:
Tertiary referral center, hospital.
Patients:
Facial photos taken from 128 patients with facial paralysis and two persons with no history of facial palsy were analyzed.
Intervention:
Diagnostic.
Main Outcome Measures:
Correlation with Sunnybrook (SB) and House-Brackmann (HB) grading scales.
Results:
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.
Conclusions:
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.
