Towards automated pose invariant 3D dental biometrics

A novel pose invariant 3D dental biometrics framework is proposed for human identification by matching dental plasters in this paper. Using 3D overcomes a number of key problems that plague 2D methods. As best as we can tell, our study is the first attempt at 3D dental biometrics. It includes a multi-scale feature extraction algorithm for extracting pose invariant feature points and a triplet-correspondence algorithm for pose estimation. Preliminary experimental result achieves 100% rank-1 accuracy by matching 7 postmortem (PM) samples against 100 ante-mortem (AM) samples. In addition, towards a fully automated 3D dental identification testing, the accuracy achieves 71.4% at rank-1 accuracy and 100% at rank-4 accuracy. Comparing with the existing algorithms, the feature point extraction algorithm and the triplet-correspondence algorithm are faster and more robust for pose estimation. In addition, the retrieval time for a single subject has been significantly reduced. Furthermore, we discover that the investigated dental features are discriminative and useful for identification. The high accuracy, fast retrieval speed and the facilitated identification process suggest that the developed 3D framework is more suitable for practical use in dental biometrics applications in the future. Finally, the limitations and future research directions are discussed.