Abstract
Human identification using tooth plays a crucial role in disaster victim identification. Traditional tooth recognition methods like iterative closest point (ICP) require laborious pairwise registration, so in this paper we focus on deep learning methods for human identification using tooth. We propose a complete workflow for tooth segmentation and recognition based on PointNet++ using the 3D intraoral scanning (IOS) model. Our method consists of two main components: an improved PointNet++ based tooth segmentation approach and a tooth recognition method that combines curvature feature extraction with improved PointNet++ using the segmented tooth part. To evaluate the identification method, we collect 240 IOS models, in which 208 models are used for training, and 32 models acquired for the second time are used for testing. The experimental results achieve a recognition accuracy of 96.88% on the test set, which demonstrates the potential of using the IOS model and deep learning methods for fully-automatedly human identification using tooth.
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Liu, X., Yuan, L., Jiang, C., JiannanYu, Li, Y. (2023). Human Identification Using Tooth Based on PointNet++ . In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_13
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DOI: https://doi.org/10.1007/978-981-99-8565-4_13
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