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Face comparison analysis of patients with orthognathic surgery treatment using cloud computing-based face recognition
Filiz Aydoğan Akgün1, Yavuz Fındık2, Serdar Solak3
1Department of Orthodontics, Faculty of Dentistry, Burdur Mehmet Akif Ersoy University, Burdur, Turkey.
Summary
Facial recognition software showed high similarity scores after orthognathic surgery, indicating subtle aesthetic changes. Smiling photographs yielded higher scores, suggesting algorithms prioritize upper-face landmarks in analysis.
Area of Science:
- Medical imaging
- Computer vision
- Plastic surgery
Background:
- Orthognathic surgery alters facial aesthetics, necessitating objective evaluation methods.
- Commercial facial recognition systems offer potential tools for assessing surgical outcomes.
Purpose of the Study:
- To evaluate the ability of Microsoft Azure, Amazon Web Services Rekognition, and Face++ to detect postoperative changes after orthognathic surgery.
- To compare the performance of different facial recognition APIs in analyzing facial aesthetics post-surgery.
Main Methods:
- Analysis of 35 patients' preoperative and postoperative photographs using three cloud-based facial recognition APIs.
- Comparison of similarity scores across different facial expressions (relaxed, smiling) and views (profile, semiprofile).
- Statistical analysis of patient characteristics and surgical factors influencing similarity scores.
Main Results:
- Face++ yielded lower similarity scores compared to Microsoft Azure and Amazon Web Services Rekognition.
- Smiling photographs resulted in higher similarity scores, with statistically significant differences observed across programs (P < 0.05).
- No significant impact of sex, type of surgery, or surgical approach on similarity scores was found across all programs.
Conclusions:
- High similarity scores suggest that current facial recognition algorithms may overemphasize upper-face landmarks.
- Facial recognition software shows potential for objective assessment of orthognathic surgery outcomes, though algorithm biases may exist.
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