The goal of forensic dentistry is to identify individuals based on their dental characteristics. In this paper, we introduce a system that uses some scenarios to fuse three matching techniques for identifying individuals based on their dental X-ray images. The system integrates a method for teeth segmentation, and three different methods for representing and matching teeth. The first method for matching antemortem (AM) and postmortem (PM) images represents each tooth contour by a set of signature vectors obtained at salient points on the contour of the tooth. The second method uses hierarchical chamfer distance for matching AM and PM teeth to reduce the search space and accordingly reduce the retrieval time. The third matching method represents each tooth by a small set of features extracted using the forcefield energy function and Fourier descriptors. For each matcher, given a query PM image, AM radiographs that are mostly similar to the PM image, are found and presented to the user. To improve the performance of the system, we present different scenarios to fuse the three matchers. We fuse the matchers using three different approaches at the matching level, the decision level, and using the Bayesian framework. Preliminarily results demonstrate that fusing the matching techniques improves the overall performance of the dental identification system.

The goal of forensic dentistry is to identify individuals based on their dental characteristics. In this paper we present a system for identifying individuals from their dental X-ray records. Given a dental record, usually a postmortem (PM) radiograph, the system searches a database of ante mortem (AM) radiographs and retrieves the best matches from the database. The system automatically segments dental X-ray images into individual teeth and extracts representative feature vectors for each tooth, which are later used for retrieval. The system integrates one method for teeth segmentation, and two different methods for representing and matching teeth. The first matching method represents each tooth contour by signature vectors obtained at salient points on the contour of the tooth. The second method uses hierarchical Chamfer distance for matching AM and PM teeth to reduce the search space and accordingly reduce the retrieval time. Given a query PM image, and according to a matching distance, AM radiographs that are most similar to the PM image, are found and presented to the user using the two matching methods. The experimental results show that the system is robust. We studied the performance of the different modules of the system as well as the results effusing the matching techniques.

Dental biometrics deal with human identification from dental characteristics. In this paper, we present a new technique for identifying people based upon shapes and appearances of their teeth from dental X-ray radiographs. The new technique represents each tooth by a feature vector obtained from the forcefield energy function of the grayscale image of the tooth and Fourier descriptors of the contour of the tooth. The feature vector is composed of the distances between a small number of potential energy wells as well as a small number of Fourier descriptors. Given a query image (i.e., postmortem radiograph), each tooth is matched with the archived teeth in the database (antemortem radiographs) that have the same tooth number. Then, voting is used to obtain a list of best matches for the query image based upon the matching results of the individual teeth. Our goal of using appearance and shape-based features together is to overcome the drawback of using only the contour of the tooth, which can be strongly affected by the quality of the images. The experimental results on a database of 162 antemortem images show that our method is effective in identifying individuals based on their dental radiographs

The goal of forensic dentistry is to identify individuals based on their dental characteristics. In this paper we present a new matching technique for identifying missing, and wanted individuals from their dental X-ray records. Given a dental record, usually a postmortem (PM) radiograph, the proposed technique searches a database of ante mortem (AM) radiographs and retrieves the best matches from the database. The technique is based on matching teeth contours using hierarchical Chamfer distance. The proposed technique has two main stages: feature extraction, and teeth matching. During retrieval, according to a matching distance between the AM and PM teeth, AM radiographs that are most similar to a given PM image, are found and presented to the user. The experimental results on a database of 162 AM images show that the technique is robust for identifying individuals based on their dental records.