Digital Dental Biometrics for Human Identification Based on Automated 3D Point Cloud Feature Extraction and Registration.

BACKGROUND: Intraoral scans (IOS) provide precise 3D data of dental crowns and gingival structures. This paper explores an application of IOS in human identification. METHODS: We propose a dental biometrics framework for human identification using 3D dental point clouds based on machine learning-related algorithms, encompassing three stages: data preprocessing, feature extraction, and registration-based identification. In the data preprocessing stage, we use the curvature principle to extract distinguishable tooth crown contours from the original point clouds as the holistic feature identification samples. Based on these samples, we construct four types of local feature identification samples to evaluate identification performance with severe teeth loss. In the feature extraction stage, we conduct voxel downsampling, then extract the geometric and structural features of the point cloud. In the registration-based identification stage, we construct a coarse-to-fine registration scheme in order to realize the identification task. RESULTS: Experimental results on a dataset of 160 individuals demonstrate that our method achieves a Rank-1 recognition rate of 100% using complete tooth crown contours samples. Utilizing the remaining four types of local feature samples yields a Rank-1 recognition rate exceeding 96.05%. CONCLUSIONS: The proposed framework proves effective for human identification, maintaining high identification performance even in extreme cases of partial tooth loss.

3D superimposition of human dentition contours in personal identification: A preliminary study.

The human permanent dentition has been commonly used for personal identification due to its uniqueness. Limited research, however, is conducted using 3D digital dental models. We propose to develop a new 3D superimposition method using the contours of human dentition and to further evaluate its feasibility. A total of 270 intraoral scan models were collected from 135 subjects. After a one-year interval, 52 subjects were chosen at random and the secondary intraoral scan models were obtained. The dentition contours of the first and secondary models were extracted to form a resource dataset and a test dataset. Through the application of the iterative nearest point (ICP) algorithm, the test dataset was registered with the resource dataset, and the root mean square error (RMSE) values of the point-to-point distances were calculated. 104 genuine pairs and 13,936 imposter pairs were generated, and in this study, the registration accuracy was 100%. The difference between mean RMSE values for the genuine pair (0.20 ± 0.06 mm) and the minimum RMSE value for the imposter pair (0.83 ± 0.06 mm) was significant in the maxillary arch (p < 0.05). Similarly, in the mandibular arch, the difference between mean RMSE values for the genuine pair (0.22 ± 0.07 mm) and the minimum RMSE value for the imposter pair (0.85 ± 0.08 mm) was significant (p < 0.05). The difference between the RMSE value for the genuine pair in the maxillary and the mandibular arch was significant (p < 0.05). This study indicated the feasibility of dentition contour-based model superimposition and could be considered for personal identification in the future.

[Three-dimensional localization and assessment of maxillary palatal impacted canines with cone-beam computed tomography].

PURPOSE: To evaluate the location of maxillary palatal impacted canines and resorption of neighboring incisors with cone-beam computed tomography (CBCT). METHODS: Twenty-two healthy adolescent patients who had received orthodontic treatments at Stomatological Hospital of Nanjing Medical University were selected and scanned by CBCT. Palatal impacted maxillary canines were reconstructed by Dolphin imaging 11.0 software. The impactions, spatial relationships and classification relative to adjacent structures and incisor resorption were assessed. RESULTS: Most of the maxillary palatal impacted canines inclinated mesially and palatally. Mesial malpositions were more significantly prevalent in Class I and IV, and the prevalence rates were 30.8% and 38.5% respectively. Mesial inclinations of the impacted canines to occlusal plane were mostly between 53.8° and 68.5°, and the distances from the impacted canines to median sagittal plane were between 5.4 and 8.4 mm. Older the patient was, further the impacted canines mesiopalatal displaced and mesial inclined. The roots of 84.6% of lateral incisors and 19.2% of central incisors contacted impacted canines; Root resorption occurred in 50% of lateral incisors and 15.4% of central incisors, which predominantly located in apical third of the lateral incisors and middle third of the central incisors. A inverse correlation was found between the resorption rates of adjacent incisors and minimum distances from impacted canines to adjacent incisors. CONCLUSIONS: CBCT allows three dimensional evaluation of impaction and spatial relationships relative to adjacent structures. In addition, 3 dimensional measurement contributes to more accurate exhibition of the adjacent root resorptions, inclinations and depths of the impacted canines, which leads to more efficient guidance of maxillary palatal impacted canines treatment.