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

被引:0
|
作者
Zhou, Yu [1 ,2 ]
Yuan, Li [1 ,2 ]
Li, Yanfeng [3 ]
Yu, Jiannan [3 ,4 ,5 ]
机构
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, 30 Xueyuan Rd, Beijing 100083, Peoples R China
[2] Minist Educ, Key Lab Knowledge Automat Ind Proc, 30 Xueyuan Rd, Beijing 100083, Peoples R China
[3] Chinese Peoples Liberat Army Gen Hosp, Med Ctr 4, Dept Stomatol, 51 Fucheng Rd, Beijing 100048, Peoples R China
[4] Chinese Peoples Liberat Army Gen Hosp, Med Ctr 6, Dept Stomatol, 6 Fucheng Rd, Beijing 100048, Peoples R China
[5] Chinese PLA Med Sch, 28 Fuxing Rd, Beijing 100853, Peoples R China
来源
BIOENGINEERING-BASEL | 2024年 / 11卷 / 09期
基金
北京市自然科学基金;
关键词
dental biometrics; 3D dental point cloud; human identification; coarse-to-fine registration; machine learning;
D O I
10.3390/bioengineering11090873
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
引用
收藏
页数:19
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