Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2023

Abstract

The dental arch is a significant anatomical feature that is crucial in assessing tooth arrangement and configuration and has a potential for human identification in biometrics and digital forensic dentistry. In a previous study, we proposed an auto pose-invariant arch feature extraction Radial Ray Algorithm (RRA) and a matching framework [1] based solely on 3D dental geometry. To enhance the identification accuracy and speed of our previous work, we propose in this study a transformer architecture that can extract dental keypoints by encoding both local and global features. The dental arch is then constructed through robust interpolation of the dental keypoints using B-Spline and is compared using the same identification framework. To evaluate the effectiveness of our proposed approach, we conducted experiments by matching the same 11 post-mortems (PM) samples against 200 antemortem (AM) samples. Our results show that our approach achieves higher accuracy and faster speed compared to our previous work. Specifically, 11 samples (100%) achieved a top 6.5% (13/200) accuracy out of the 200-rank list, compared to the top 15.5% (31/200) accuracy previously. Additionally, the time required to identify a single subject from 200 subjects has been reduced from 5 minutes to 3 minutes. The dental arch can be used as a powerful filter feature. Our findings make a significant contribution to the existing literature on dental identification and demonstrate the potential practical applications of our approach in diverse fields such as biometrics, forensic dentistry, orthodontics, and anthropology.

Keywords

3D Dental Biometrics, Dental Arch, Keypoint Detection, Transformer, Human Identification

Discipline

Artificial Intelligence and Robotics

Research Areas

Information Systems and Management

Publication

Proceedings of the 2023 IEEE Conference on Artificial Intelligence (CAI), Santa Clara, California, USA, June 5-6

First Page

139

Last Page

140

ISBN

9798350339840

Identifier

10.1109/CAI54212.2023.00067

Publisher

IEEE

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/CAI54212.2023.00067

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