Changes in facial recognition analysis after orthognathic surgery
- 1Discipline of Oral & Maxillofacial Surgery, Faculty of Dentistry, National University of Singapore
- 2Biostatistics Unit, Faculty of Dentistry, National University of Singapore
- 3Instituto Bucomaxilofacial
Abstract
Malocclusion is a prevalent condition worldwide. When associated with an underlying Skeletal Class 2 or 3 discrepancy, malocclusion may require treatment beyond orthodontic correction. Orthognathic Surgery (OGS) is a routine treatment to correct dentofacial deformity which results in changes of patient’s facial form in all dimensions. As Artificial intelligence (AI) prevails in the field of biometric facial recognition, knowledge on the ability of AI-based facial recognition to recognize patients who have undergone OGS should be kept in tandem with its rising reliance. The objective of the study is to investigate if modern facial recognition software can detect facial changes in patients after OGS. This cohort study included 39 patients who had undergone OGS and 39 control subjects, recruited from a tertiary center. Changes in hard and soft tissue landmarks of OGS patients were recorded using 3-dimensional (3D) stereophotogrammetry and lateral cephalograms. Pre- and post-surgery frontal photos were analyzed with four commercially available facial recognition software to produce facial Similarity Scores (SS) between the two sets of frontal photos. Differences between SS of study and control subjects were investigated. Correlation with patient demographics and skeletal diagnosis, surgical type and magnitude, presence of asymmetry, and soft and hard tissue facial changes were analyzed. There was a statistically significant difference in the SS of patients who underwent OGS compared to the controls across all four software (P < 0.001). Maxillary advancement surgery, changes to External Canthus-Alar (ExC-Alar) point, Paranasal point, A-point to Vertical Reference Plane (A-VRP) and hard tissue Pogonion point were found to significantly lower SS. Dlib face_recognition was most sensitive to post-surgery facial changes among the four software. Despite significant changes to the SS, the patients were successfully recognized as the same person by the software. However, patients with substantial increases in projection at the cheek, paranasal, and chin, are potentially vulnerable to recognition failure.
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BibTeX
@article{ng2026changes,
title = {{Changes in facial recognition analysis after orthognathic surgery}},
author = {Almond Yi Qi Ng and Chee Weng Yong and Yu Fan Sim and Lei Zheng and Majeed Rana and Jonathas Daniel Paggi Claus and Raymond Chung Wen Wong},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62776-w},
url = {https://doi.org/10.1038/s41598-026-62776-w},
note = {CC BY},
}
RIS
TY - JOUR
TI - Changes in facial recognition analysis after orthognathic surgery
AU - Almond Yi Qi Ng
AU - Chee Weng Yong
AU - Yu Fan Sim
AU - Lei Zheng
AU - Majeed Rana
AU - Jonathas Daniel Paggi Claus
AU - Raymond Chung Wen Wong
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62776-w
UR - https://doi.org/10.1038/s41598-026-62776-w
AB - Malocclusion is a prevalent condition worldwide. When associated with an underlying Skeletal Class 2 or 3 discrepancy, malocclusion may require treatment beyond orthodontic correction. Orthognathic Surgery (OGS) is a routine treatment to correct dentofacial deformity which results in changes of patient’s facial form in all dimensions. As Artificial intelligence (AI) prevails in the field of biometric facial recognition, knowledge on the ability of AI-based facial recognition to recognize patients who have undergone OGS should be kept in tandem with its rising reliance. The objective of the study is to investigate if modern facial recognition software can detect facial changes in patients after OGS. This cohort study included 39 patients who had undergone OGS and 39 control subjects, recruited from a tertiary center. Changes in hard and soft tissue landmarks of OGS patients were recorded using 3-dimensional (3D) stereophotogrammetry and lateral cephalograms. Pre- and post-surgery frontal photos were analyzed with four commercially available facial recognition software to produce facial Similarity Scores (SS) between the two sets of frontal photos. Differences between SS of study and control subjects were investigated. Correlation with patient demographics and skeletal diagnosis, surgical type and magnitude, presence of asymmetry, and soft and hard tissue facial changes were analyzed. There was a statistically significant difference in the SS of patients who underwent OGS compared to the controls across all four software (P < 0.001). Maxillary advancement surgery, changes to External Canthus-Alar (ExC-Alar) point, Paranasal point, A-point to Vertical Reference Plane (A-VRP) and hard tissue Pogonion point were found to significantly lower SS. Dlib face_recognition was most sensitive to post-surgery facial changes among the four software. Despite significant changes to the SS, the patients were successfully recognized as the same person by the software. However, patients with substantial increases in projection at the cheek, paranasal, and chin, are potentially vulnerable to recognition failure.
SN - 2045-2322
ER -