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Three-dimensional superimposition of digital models for individual identification.
Qing-Nan Mou1, Ling-Ling Ji1, Yan Liu2
1Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, 710004, Shaanxi, PR China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an, 710004, Shaanxi, PR China.
Forensic Science International
|December 6, 2020
Summary
This study developed a 3D digital model method for accurate human identification using dental scans. The technique achieved 100% accuracy in matching antemortem and postmortem dental records.
Area of Science:
- Forensic Science
- Biometrics
- Digital Imaging
Background:
- Human dentition serves as a unique identifier, yet 3D digital models are underutilized for personal identification.
- Establishing reliable methods for individual identification is crucial in forensic investigations.
Purpose of the Study:
- To develop and evaluate a novel method for individual identification using three-dimensional (3D) digital dental models.
- To assess the feasibility of a 3D image registration algorithm for forensic identification applications.
Main Methods:
- Acquired 3D digital models from dental casts and intraoral scans of 28 subjects at different time points.
- Utilized a 3D image registration algorithm incorporating correntropy to compare antemortem and postmortem dentition models.
- Calculated point-to-point root mean square (RMS) distances to quantify differences between 3D dental models.
Main Results:
- The algorithm successfully distinguished between matched (0.18±0.03mm RMS) and mismatched (1.04±0.67mm RMS) dental models with statistical significance (P<0.05).
- All correctly matched models exhibited RMS values below 0.27mm.
- The identification method achieved 100% accuracy in this study.
Conclusions:
- The developed 3D superimposition method based on digital dental models is a highly effective tool for personal identification.
- This technique offers a promising, accurate, and potentially automated approach for forensic identification using dentition.
