Personal identification via matching of curved multiplanar computed tomography reconstructions and panoramic radiographs

Computer vision (CV)-based personal identification enables automated matching of recent radiological images with clinical databases to identify unknown individuals. This study aimed to assess whether a panoramic radiograph (PR)-like image reconstructed from computed tomography (CT) data using curved multiplanar reconstruction could enable CV-based personal identification using a PR database. A method was developed to automatically generate PR-like images with adjustable parameters, based on 50 CT examinations including the jaw region (38.64 ± 16.72 years; 17 females, 33 males), allowing for variations such as tooth rotations. Systematic modification of parameters enabled the generation of different representations to determine optimal settings for a large number of individuals. Multiple PR-like images per identity were tested against a PR database containing 82,036 PRs from 43,379 individuals. Utilizing the most effective individual parameter settings, 72% (36/50) of individuals were correctly identified at rank 1, 82% (41/50) at rank 10, and 96% (48/50) at rank 100 - out of 43,379 possible individuals. The rank describes the position of the matched image in a list sorted after a descending similarity score. When the optimal parameters were applied to a larger number of individuals, the identification rates were 50% (25/50) at rank 1, 64% (32/50) at rank 10, and 78% (39/50) at rank 100. In conclusion, CV demonstrates potential for personal identification by comparing automatically generated PR-like images with a large PR database.