Human Identification with Panoramic Dental Images using Mask R-CNN and SURF

Abstract

There exist unidentified bodies after natural disasters, terrorist attacks, and accidents. DNA, fingerprint, and dental information are biometrics that is commonly used by forensic experts to identify the dead bodies. The dental image of an unidentified body is compared with the dental image records taken before death to determine whether both dental images correspond to the same individual. In this study, a new method is presented for human identification with dental biometrics using panoramic X-ray images. The proposed method employs Mask R-CNN and Speeded up Robust Features (SURF) for matching the dental images. The method follows a tooth-wise matching approach that evaluates the correspondence of each tooth individually. The teeth in dental images are segmented and classified with Mask R-CNN. The SURF key points of each tooth in the unidentified dental image are compared with the teeth in the database containing previously captured dental images. The final identification is performed by bringing the tooth-wise scores together. Mask R-CNN is tested on 8896 tooth images and human identification with SURF is tested on 102 panoramic dental images of 51 different individuals taken at different times. The experimental results show promising outcomes with a matching percentage of 80.39% at first rank.