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

• Main Authors: ZHANG, Zhiyuan, ONG, Sim Heng, ZHONG, Xin, FOONG, Kelvin W. C.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Dental biometrics; Tooth recognition; Postmortem identification; Shape descriptor; Keypoint detection; Shape matching; Random Forest; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
• 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
• Institution: Singapore Management University

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...