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.