3D dental similarity quantification in forensic odontology identification

Anika Kofod Petersen; Andrew Forgie; Palle Villesen; Line Staun Larsen

doi:10.1016/j.forsciint.2025.112462

3D dental similarity quantification in forensic odontology identification

Anika Kofod Petersen^*, Andrew Forgie, Palle Villesen, Line Staun Larsen

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Abstract

Forensic odontology identification largely depends on comparing dental work like fillings and crowns with the dental records of potential victims. This process can be challenging, especially regarding victims with minimal or no dental work. Alternatively, 3D tooth morphology can be used for identification by automated dental surface similarity scoring. However, high-resolution 3D intraoral photo scans contain hundreds of thousands of datapoints from each individual jaw, making database searches difficult and slow. Here, we reduce full 3D scans to keypoints, which are small points located in areas of high curvature on tooth surfaces. We use Difference of Curvature (DoC) for robust keypoint detection and evaluate different keypoint representation methods to distinguish between scans of the same individual and scans of different individuals, assigning them a similarity score.The results demonstrate that combining DoC with the Signature of Histograms of OrienTations (SHOT) representation method effectively separates matches from mismatches. This indicates the potential for automatic scoring of dental surface similarity. This can be valuable for forensic odontology identification, especially in cases where traditional methods are limited by the lack of dental work.

Original language	English
Article number	112462
Journal	Forensic Science International
Volume	370
ISSN	0379-0738
DOIs	https://doi.org/10.1016/j.forsciint.2025.112462
Publication status	Published - May 2025

Keywords

3D dental comparison
Biometric identification
Disaster victim identification
Forensic odontology identification
Keypoint description
Keypoint detection

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10.1016/j.forsciint.2025.112462Licence: CC BY

https://doi.org/10.1016/j.forsciint.2025.112462

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@article{e5d02f2e679044189ae087d635280d8f,

title = "3D dental similarity quantification in forensic odontology identification",

abstract = "Forensic odontology identification largely depends on comparing dental work like fillings and crowns with the dental records of potential victims. This process can be challenging, especially regarding victims with minimal or no dental work. Alternatively, 3D tooth morphology can be used for identification by automated dental surface similarity scoring. However, high-resolution 3D intraoral photo scans contain hundreds of thousands of datapoints from each individual jaw, making database searches difficult and slow. Here, we reduce full 3D scans to keypoints, which are small points located in areas of high curvature on tooth surfaces. We use Difference of Curvature (DoC) for robust keypoint detection and evaluate different keypoint representation methods to distinguish between scans of the same individual and scans of different individuals, assigning them a similarity score.The results demonstrate that combining DoC with the Signature of Histograms of OrienTations (SHOT) representation method effectively separates matches from mismatches. This indicates the potential for automatic scoring of dental surface similarity. This can be valuable for forensic odontology identification, especially in cases where traditional methods are limited by the lack of dental work.",

keywords = "3D dental comparison, Biometric identification, Disaster victim identification, Forensic odontology identification, Keypoint description, Keypoint detection",

author = "{Kofod Petersen}, Anika and Andrew Forgie and Palle Villesen and {Staun Larsen}, Line",

year = "2025",

month = may,

doi = "10.1016/j.forsciint.2025.112462",

language = "English",

volume = "370",

journal = "Forensic Science International",

issn = "0379-0738",

publisher = "Elsevier Ireland Ltd",

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T1 - 3D dental similarity quantification in forensic odontology identification

AU - Kofod Petersen, Anika

AU - Forgie, Andrew

AU - Villesen, Palle

AU - Staun Larsen, Line

PY - 2025/5

Y1 - 2025/5

N2 - Forensic odontology identification largely depends on comparing dental work like fillings and crowns with the dental records of potential victims. This process can be challenging, especially regarding victims with minimal or no dental work. Alternatively, 3D tooth morphology can be used for identification by automated dental surface similarity scoring. However, high-resolution 3D intraoral photo scans contain hundreds of thousands of datapoints from each individual jaw, making database searches difficult and slow. Here, we reduce full 3D scans to keypoints, which are small points located in areas of high curvature on tooth surfaces. We use Difference of Curvature (DoC) for robust keypoint detection and evaluate different keypoint representation methods to distinguish between scans of the same individual and scans of different individuals, assigning them a similarity score.The results demonstrate that combining DoC with the Signature of Histograms of OrienTations (SHOT) representation method effectively separates matches from mismatches. This indicates the potential for automatic scoring of dental surface similarity. This can be valuable for forensic odontology identification, especially in cases where traditional methods are limited by the lack of dental work.

AB - Forensic odontology identification largely depends on comparing dental work like fillings and crowns with the dental records of potential victims. This process can be challenging, especially regarding victims with minimal or no dental work. Alternatively, 3D tooth morphology can be used for identification by automated dental surface similarity scoring. However, high-resolution 3D intraoral photo scans contain hundreds of thousands of datapoints from each individual jaw, making database searches difficult and slow. Here, we reduce full 3D scans to keypoints, which are small points located in areas of high curvature on tooth surfaces. We use Difference of Curvature (DoC) for robust keypoint detection and evaluate different keypoint representation methods to distinguish between scans of the same individual and scans of different individuals, assigning them a similarity score.The results demonstrate that combining DoC with the Signature of Histograms of OrienTations (SHOT) representation method effectively separates matches from mismatches. This indicates the potential for automatic scoring of dental surface similarity. This can be valuable for forensic odontology identification, especially in cases where traditional methods are limited by the lack of dental work.

KW - 3D dental comparison

KW - Biometric identification

KW - Disaster victim identification

KW - Forensic odontology identification

KW - Keypoint description

KW - Keypoint detection

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DO - 10.1016/j.forsciint.2025.112462

M3 - Journal article

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AN - SCOPUS:105001866397

SN - 0379-0738

VL - 370

JO - Forensic Science International

JF - Forensic Science International

M1 - 112462

ER -

3D dental similarity quantification in forensic odontology identification

Abstract

Keywords

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