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 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.