MultiDentFormer: Local-to-Global Feature Fusion for 3D Dental Biometric Recognition

Forensic odontology research has shown that teeth are the hardest structures in the human body and can serve as reliable indicators for individual identification. In this study, we propose a Point Transformer based algorithm called MultiDentFormer, which leverages local-to-global feature fusion to achieve 3D dental biometric recognition. The global features refer to the high-level semantic representations of the full dental arch, capturing the overall structure and arrangement of the teeth, while the local features focus on the detailed geometry of individual teeth. To address the limited capability of the original Point Transformer in capturing local information at the early stages of the network, this study introduces a single-point local feature aggregation module. This module aggregates local geometric information and low-level feature representations into each local center point, thereby enhancing the expressiveness of the extracted features. Additionally, to address the issue of detail information loss during the network downsampling process, which weakens feature representation capability, this paper introduces a multi-scale feature fusion strategy. This strategy effectively integrates the rich detail features from shallow layers with the high-level semantic features extracted from deeper layers, compensating for the information lost during downsampling. Experimental evaluations on a IOS dataset comprising 104 individuals and a CBCT dataset with 51 individuals demonstrate that MultiDentFormer achieves superior recognition performance compared to common algorithms in 3D point cloud classification. The local feature aggregation strategy and multi-scale feature fusion strategy enhance the discriminative ability and classification performance of the 3D point cloud feature representations.