J Korean Assoc Oral Maxillofac Surg.  2022 Aug;48(4):201-206. 10.5125/jkaoms.2022.48.4.201.

Changes in a facial recognition algorithm following different types of orthognathic surgery: a comparative study

Affiliations
  • 1Department of Oral and Maxillofacial Surgery, Dankook University Dental Hospital, Cheonan, Korea

Abstract


Objectives
Contemporary biometric technologies have been gaining traction in both public and private security sectors. Facial recognition is the most commonly used biometric technology for this purpose. We aimed to evaluate the ability of a publicly available facial recognition application program interface to calculate similarity scores of presurgical and postsurgical photographs of patients who had orthognathic surgery.
Materials and Methods
Presurgical and postsurgical photographs of 75 patients who had orthognathic surgery between January 2018 and November 2020 in our department were used. Frontal photographs of patients in relaxed and smiling states were taken. The patients were classified into three groups: Group 2 had one-jaw surgery, Group 3 had two-jaw surgery to correct mandibular prognathism, and Group 4 had two-jaw surgery to correct facial asymmetry. For comparison, photographs of 10 participants were used as controls (Group 1). Two facial recognition application programs (Face X and Azure) were used to assess similarity scores.
Results
The similarity scores in the two programs showed significant results. The similarity score of the control group, which did not undergo orthognathic surgery, was the highest. The results for Group 2, Group 3, and Group 4 were higher in the order of Group 2, Group 3, and Group 4.
Conclusion
In this study, all orthodontic patients were recognized as the same person using the face recognition program before and after surgery. A significant difference in similarity results was obtained between the groups with both Face X and Azure and in both relaxed and smiling states.

Keyword

Facial recognition; Algorithm; Orthognathic surgery; Photographs

Figure

  • Fig. 1 Example facial photographs of each group: (A) Group 1 patient, (B) Group 2 patient, (C) Group 3 patient, and (D) Group 4 patient. (Group 1: the control group, Group 2: the group that had one-jaw surgery, Group 3: the group that had two-jaw surgery for mandibular protrusion, Group 4: the group that had two-jaw surgery for facial asymmetry)

  • Fig. 2 Landmarks analyzed by the Face X program.

  • Fig. 3 Landmarks analyzed by the Azure program.


Reference

References

1. Esan OA, Zuva T, Ngwira SM, Zuva K. 2013; Performance improvement of authentication of fingerprints using enhancement and matching algorithms. Int J Emerg Technol Adv Eng. 3:472–82.
2. Dhameliya MD, Chaudhari JP. 2013; A multimodal biometric recognition system based on fusion of palmprint and fingerprint. IJETT. 4:1908–11.
3. Birgale L, Kokare M. 2010; Iris recognition without iris normalization. J Comput Sci. 6:1042–7. https://doi.org/10.3844/JCSSP.2010.1042.1047. DOI: 10.3844/jcssp.2010.1042.1047.
Article
4. Moghaddam HA, Ghayoumi M. 2006. Facial image feature extraction using support vector machines. In VISAPP. p. 480–5. DOI: 10.5220/0001363604800485.
5. Fontaine X, Achanta R, Süsstrunk S. 2017; Face recognition in real-world images. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1482–6. https://doi.org/10.1109/ICCCSP.2018.8452853. DOI: 10.1109/ICCCSP.2018.8452853. PMCID: PMC6419773.
Article
6. Adjabi I, Ouahabi A, Benzaoui A, Taleb-Ahmed A. 2020; Past, present, and future of face recognition: a review. Electronics. 9:1188. https://doi.org/10.3390/electronics9081188. DOI: 10.3390/electronics9081188.
Article
7. Dragon CB, Shroff B, Carrico C, Stilianoudakis S, Strauss R, Lindauer SJ. 2020; The effect of orthognathic surgery on facial recognition algorithm analysis. Am J Orthod Dentofacial Orthop. 158:84–91. https://doi.org/10.1016/j.ajodo.2019.11.013. DOI: 10.1016/j.ajodo.2019.11.013. PMID: 32448566.
Article
8. Singh S, Prasad SVAV. 2018; Techniques and challenges of face recognition: a critical review. Procedia Comput Sci. 143:536–43. https://doi.org/10.1016/J.PROCS.2018.10.427. DOI: 10.1016/j.procs.2018.10.427.
Article
9. Naran S, Steinbacher DM, Taylor JA. 2018; Current concepts in orthognathic surgery. Plast Reconstr Surg. 141:925e–936e. https://doi.org/10.1097/PRS.0000000000004438. DOI: 10.1097/PRS.0000000000004438. PMID: 29794714.
Article
10. Jung J, Lee CH, Lee JW, Choi BJ. 2018; Three dimensional evaluation of soft tissue after orthognathic surgery. Head Face Med. 14:21. https://doi.org/10.1186/s13005-018-0179-z. DOI: 10.1186/s13005-018-0179-z. PMID: 30290762. PMCID: PMC6173856.
Article
11. Agrawal AK, Singh YN. 2015; Evaluation of face recognition methods in unconstrained environments. Procedia Comput Sci. 48:644–51. https://doi.org/10.1016/j.procs.2015.04.147. DOI: 10.1016/j.procs.2015.04.147.
Article
Full Text Links
  • JKAOMS
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr