Dental Identification Based on Single Tooth Feature Concatenation
Forensic dentistry research indicates that teeth, as one of the hardest tissues in the human body, can be well-preserved even in disasters and crime scenes, providing a reliable means of human identification. Dental identification based on 3D tooth point clouds has made some progress recently. But t...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11071270/ |
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| author | Li Yuan Luoji Zhu Kang-Hyun Jo Yanfeng Li |
| author_facet | Li Yuan Luoji Zhu Kang-Hyun Jo Yanfeng Li |
| author_sort | Li Yuan |
| collection | DOAJ |
| description | Forensic dentistry research indicates that teeth, as one of the hardest tissues in the human body, can be well-preserved even in disasters and crime scenes, providing a reliable means of human identification. Dental identification based on 3D tooth point clouds has made some progress recently. But the whole-dentition-based identification mode may fail in complex forensic application scenarios such as tooth loss, changes in arrangement or scattering. Therefore, we propose a dental identification deep neural network based on single tooth feature concatenation in this paper, offering more flexibility and accuracy as it can adapt to various tooth arrangements and different tooth numbers. To address the challenges of difficult samples with fewer teeth, a progressive random tooth dropout training strategy is devised to aid learning. For inadequate discrimination of individual tooth features, PointResNet is introduced as a feature extraction module. To handle significant differences in samples with varying tooth numbers and arrangements from the same individual, the additive angular margin loss is adopted. To verify the effectiveness of our method in various dental conditions, we apply random spatial transformations to the teeth of the same individual and simulate different cases of missing teeth on our self-constructed dataset. The results from comparative and ablation experiments confirmed the effectiveness of this approach. |
| format | Article |
| id | doaj-art-3976f05af64144088d26f3f13d42e1a6 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3976f05af64144088d26f3f13d42e1a62025-08-20T03:13:39ZengIEEEIEEE Access2169-35362025-01-011312279212280310.1109/ACCESS.2025.358554511071270Dental Identification Based on Single Tooth Feature ConcatenationLi Yuan0https://orcid.org/0009-0007-6172-6322Luoji Zhu1https://orcid.org/0000-0001-9836-7115Kang-Hyun Jo2https://orcid.org/0000-0002-4937-7082Yanfeng Li3School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaDepartment of Stomatology, Chinese PLA General Hospital Fourth Medical Center, Beijing, ChinaForensic dentistry research indicates that teeth, as one of the hardest tissues in the human body, can be well-preserved even in disasters and crime scenes, providing a reliable means of human identification. Dental identification based on 3D tooth point clouds has made some progress recently. But the whole-dentition-based identification mode may fail in complex forensic application scenarios such as tooth loss, changes in arrangement or scattering. Therefore, we propose a dental identification deep neural network based on single tooth feature concatenation in this paper, offering more flexibility and accuracy as it can adapt to various tooth arrangements and different tooth numbers. To address the challenges of difficult samples with fewer teeth, a progressive random tooth dropout training strategy is devised to aid learning. For inadequate discrimination of individual tooth features, PointResNet is introduced as a feature extraction module. To handle significant differences in samples with varying tooth numbers and arrangements from the same individual, the additive angular margin loss is adopted. To verify the effectiveness of our method in various dental conditions, we apply random spatial transformations to the teeth of the same individual and simulate different cases of missing teeth on our self-constructed dataset. The results from comparative and ablation experiments confirmed the effectiveness of this approach.https://ieeexplore.ieee.org/document/11071270/Dental identification3D tooth point cloudsingle tooth feature concatenationPointResNet |
| spellingShingle | Li Yuan Luoji Zhu Kang-Hyun Jo Yanfeng Li Dental Identification Based on Single Tooth Feature Concatenation IEEE Access Dental identification 3D tooth point cloud single tooth feature concatenation PointResNet |
| title | Dental Identification Based on Single Tooth Feature Concatenation |
| title_full | Dental Identification Based on Single Tooth Feature Concatenation |
| title_fullStr | Dental Identification Based on Single Tooth Feature Concatenation |
| title_full_unstemmed | Dental Identification Based on Single Tooth Feature Concatenation |
| title_short | Dental Identification Based on Single Tooth Feature Concatenation |
| title_sort | dental identification based on single tooth feature concatenation |
| topic | Dental identification 3D tooth point cloud single tooth feature concatenation PointResNet |
| url | https://ieeexplore.ieee.org/document/11071270/ |
| work_keys_str_mv | AT liyuan dentalidentificationbasedonsingletoothfeatureconcatenation AT luojizhu dentalidentificationbasedonsingletoothfeatureconcatenation AT kanghyunjo dentalidentificationbasedonsingletoothfeatureconcatenation AT yanfengli dentalidentificationbasedonsingletoothfeatureconcatenation |