Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks
- Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks
- 김익재; 홍유진; Se A. Lee; Jin Kim; Jeon Mi Lee; Jong Dae Lee
- face evaluation; face symmetry; face nerve grading; face feature
- Issue Date
- Otology & neurotology
- VOL 41-1148
- 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.
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 (r¼0.905 and 0.783 for Sunnybrook and HB grading scales, respectively), while being less timeconsuming 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.
- Appears in Collections:
- KIST Publication > Article
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.