Human identification using dental radiographs is important in biometrics. Dental radiographs are mainly helpful for individual and mass disaster identification. In the 2004 tsunami, dental records were proven as the primary identifier of victims. So, this work aims to produce an automatic person identification system with shape extraction and matching techniques. For shape extraction, the available information is edge details, structural content, salient points derived from contours and surfaces, and statistical moments. Out of all these features, tooth contour information is a suitable choice here because it can provide better matching. This proposed method consists of four stages. The first step is preprocessing. The second one involves integral intensity projection for segmenting upper jaw, lower jaw, and individual tooth separately. Using connected component labeling, shape extraction was done in the third stage. The outputs obtained from the previous stage for some misaligned images are not satisfactory. So, it is improved by fast connected component labeling. The fourth stage is calculating Mahalanobis distance measure as a means of matching dental records. The matching distance observed for this method is comparatively better when it is compared with the semi-automatic contour extraction method which is our earlier work.
Dentistry can contribute for the identification of human remains after any disasters or crimes in assistance to other medical specialties. The algorithm can be developed by comparing post mortem and ante mortem dental radiographs. This work aims to introduce photographic images in addition to radiographs. In this research a contour and skeleton-based shape extraction as well as matching algorithm for dental images is proposed. An active contour model with selective binary and Gaussian filtering regularised level set method is used for contour extraction. Shape matching is done by both contour and skeleton-based approaches. The experimental results are obtained from a database of dental images include both radiographs and photographs. This algorithm provides better matching decision about the person than the existing algorithms since it includes skeleton measures also. The performance measures obtained and the hit-rate indicates that the better matching is observed with radiographic than the photographic images.
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