Texture Based Person Identification Using Dental Radiographs and Photographs in Forensic Odontology

Abstract

Forensic Odontology is the evaluation of dental information that includes ante-mortem (AM) and post-mortem (PM) radiographs for the purpose of identifying person in some grave situations such as mass fatalities, natural disasters and terrorist attacks etc. One of the key issues in using dental images is that, although both the AM and the PM radiographs belong to the same person, there may be a mismatch between those radiographs due to the missing tooth in either of the radiographs. In such a case, the missing tooth in the radiograph has to be identified prior to the matching in order to achieve accurate identification of an individual. Thus an automatic algorithm for person identification in dental radiographs and photographs is a more challenging one at present. In this paper, texture based shape extraction algorithm is taken for analysis. Distance measures and classifier based approaches are the shape matching algorithm which is used to match both AM and PM images in order to obtain exact person identification. A novel approach has to be introduced for the extraction of the missing tooth, and subsequently each tooth in the radiograph is classified using k-NN classifier with Hu’s moment invariants as feature. Then each individual tooth is separated with pulp, enamel and dentine is applied to GLCM texture features. In this paper, a novel framework has been proposed to improve the identification performance. Moreover, the proposed algorithm achieves an overall accuracy of 98% than the existing approaches.