Abstract
Dental biometrics utilizes the evidence revealed by dental radiographs for human identification. This evidence includes the tooth contours, the relative positions of neighboring teeth, and the shapes of the dental work (e.g., crowns, fillings and bridges). The proposed system has two main stages: feature extraction, and matching. The feature extraction stage uses anisotropic diffusion to enhance the images and a Mixture of Gaussians model to segment the dental work. The matching stage has three sequential steps: shape registration, computation of image similarity, and subject identification. In shape registration, we align the tooth contours and obtain the distance between them. A second method based on overlapped areas is used to match the dental work. The distance between the shapes of the teeth and the distance between the shapes of the dental work are then combined using likelihood estimates to improve the retrieval accuracy. At the second step, the correspondence of teeth between two given images is established. A distance measure based on this correspondence is then used to represent the similarity between the two images. Finally, the distances are used to infer the subject's identity.