A novel identification method using perceptual degree of concordance of occlusal surfaces calculated by a Python program

Abstract

One of the important issues during the response to a mass disaster is the identification of victims. In this study, we verified the use of the occlusal morphology of molars for individual identification. The aim of this study was to establish a simple new method for identifying individuals from molar data. Using Python, we developed programming that included the perceptual Hash (pHash) function and the Hamming distance (HD) between antemortem data (AMD) and postmortem data (PMD). The AMD comprised 2,215 dental models. The PMD were selected from the AMD set and comprised 17 models from the same individual with changes over time. As a result, 16 PMD models (over 90%) were ranked in the top 5%. Although identification using only a single molar is difficult, there is the possibility of narrowing down victims’ identity with high accuracy through verification using multiple teeth. This system is expected to be useful as a very simple method of identification.