Efficient 3D dental identification via signed feature histogram and learning keypoint detection

Current methods of dental identification are mainly based on 2D dental radiographs which suffer from speed and accuracy limitations. In this paper, we present an efficient dental identification approach based on 3D dental models. We propose a novel shape descriptor, the Signed Feature Histogram (SFH...

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Main Authors: ZHANG, Zhiyuan, ONG, Sim Heng, ZHONG, Xin, FOONG, Kelvin W. C.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/7940
https://ink.library.smu.edu.sg/context/sis_research/article/8943/viewcontent/Efficient_3D_dental_identification_via_signed_feature_histogram_and_learning_keypoint_detection.pdf
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spelling sg-smu-ink.sis_research-89432023-07-20T07:50:06Z Efficient 3D dental identification via signed feature histogram and learning keypoint detection ZHANG, Zhiyuan ONG, Sim Heng ZHONG, Xin FOONG, Kelvin W. C. Current methods of dental identification are mainly based on 2D dental radiographs which suffer from speed and accuracy limitations. In this paper, we present an efficient dental identification approach based on 3D dental models. We propose a novel shape descriptor, the Signed Feature Histogram (SFH), which is highly discriminative and can be easily computed to describe the local surface. Based on the SFH, a learning keypoint detection method is adopted to accurately detect the desired keypoints on both antemortem (AM) and postmortem (PM) models. For a given PM model, the optimal initial alignment to the AM model to be matched can be found efficiently and robustly by matching the SFHs between the keypoints. The final matching score is obtained by running the iterative closest point algorithm which further refines the initial alignment. We have performed comparative experiments for the SFH on a public dataset, and state-of-the-art performance is achieved. We also test the identification method on a database of 200 AM models and tested the performance of the proposed approach on 3 different PM datasets comprising complete, incomplete and single tooth models respectively. The experimental results show that both high accuracy and efficiency are achieved with 100% Rank-1 identification accuracy on both complete and incomplete PM models and 74% Rank-1 accuracy on single tooth PM models. The running time is only 300 s on average which is about 80 times faster than many 2D methods which can take several hours to identify one subject. 2016-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7940 info:doi/10.1016/j.patcog.2016.05.007 https://ink.library.smu.edu.sg/context/sis_research/article/8943/viewcontent/Efficient_3D_dental_identification_via_signed_feature_histogram_and_learning_keypoint_detection.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Dental biometrics Tooth recognition Postmortem identification Shape descriptor Keypoint detection Shape matching Random Forest Artificial Intelligence and Robotics Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Dental biometrics
Tooth recognition
Postmortem identification
Shape descriptor
Keypoint detection
Shape matching
Random Forest
Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
spellingShingle Dental biometrics
Tooth recognition
Postmortem identification
Shape descriptor
Keypoint detection
Shape matching
Random Forest
Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
ZHANG, Zhiyuan
ONG, Sim Heng
ZHONG, Xin
FOONG, Kelvin W. C.
Efficient 3D dental identification via signed feature histogram and learning keypoint detection
description Current methods of dental identification are mainly based on 2D dental radiographs which suffer from speed and accuracy limitations. In this paper, we present an efficient dental identification approach based on 3D dental models. We propose a novel shape descriptor, the Signed Feature Histogram (SFH), which is highly discriminative and can be easily computed to describe the local surface. Based on the SFH, a learning keypoint detection method is adopted to accurately detect the desired keypoints on both antemortem (AM) and postmortem (PM) models. For a given PM model, the optimal initial alignment to the AM model to be matched can be found efficiently and robustly by matching the SFHs between the keypoints. The final matching score is obtained by running the iterative closest point algorithm which further refines the initial alignment. We have performed comparative experiments for the SFH on a public dataset, and state-of-the-art performance is achieved. We also test the identification method on a database of 200 AM models and tested the performance of the proposed approach on 3 different PM datasets comprising complete, incomplete and single tooth models respectively. The experimental results show that both high accuracy and efficiency are achieved with 100% Rank-1 identification accuracy on both complete and incomplete PM models and 74% Rank-1 accuracy on single tooth PM models. The running time is only 300 s on average which is about 80 times faster than many 2D methods which can take several hours to identify one subject.
format text
author ZHANG, Zhiyuan
ONG, Sim Heng
ZHONG, Xin
FOONG, Kelvin W. C.
author_facet ZHANG, Zhiyuan
ONG, Sim Heng
ZHONG, Xin
FOONG, Kelvin W. C.
author_sort ZHANG, Zhiyuan
title Efficient 3D dental identification via signed feature histogram and learning keypoint detection
title_short Efficient 3D dental identification via signed feature histogram and learning keypoint detection
title_full Efficient 3D dental identification via signed feature histogram and learning keypoint detection
title_fullStr Efficient 3D dental identification via signed feature histogram and learning keypoint detection
title_full_unstemmed Efficient 3D dental identification via signed feature histogram and learning keypoint detection
title_sort efficient 3d dental identification via signed feature histogram and learning keypoint detection
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/7940
https://ink.library.smu.edu.sg/context/sis_research/article/8943/viewcontent/Efficient_3D_dental_identification_via_signed_feature_histogram_and_learning_keypoint_detection.pdf
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