Department of Radiology, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
Institute of Forensic Medicine, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
Sci Rep. 2020 Mar 2;10(1):3801. doi: 10.1038/s41598-020-60817-6.
A person may be identified by comparison between ante- and post-mortem dental panoramic radiographs (DPR). However, it is difficult to find reference material if the person is unknown. This is often the case when victims of crime or mass disaster are found. Computer vision can be a helpful solution to automate the finding of reference material in a large database of images. The purpose of the present study was to improve the automated identification of unknown individuals by comparison of ante- and post-mortem DPR using computer vision. The study includes 61,545 DPRs from 33,206 patients, acquired between October 2006 and June 2018. The matching process is based on the Speeded Up Robust Features (SURF) algorithm to find unique corresponding points between two DPRs (unknown person and database entry). The number of matching points found is an indicator for identification. All 43 individuals (100%) were successfully identified by comparison with the content of the feature database. The experimental setup was designed to identify unknown persons based on their DPR using an automatic algorithm system. The proposed tool is able to filter large databases with many entries of potentially matching partners. This identification method is suitable even if dental characteristics were removed or added in the past.
一个人可以通过比较前后的牙科全景放射照片（DPR）来识别。然而，如果这个人不为人知，就很难找到参考资料。这通常是在犯罪或大规模灾难的受害者被发现时的情况。计算机视觉可以是一个有用的解决方案，可以自动化在大型图像数据库中查找参考资料。本研究的目的是通过使用计算机视觉比较前后 DPR 来提高对未知个体的自动识别。该研究包括 61545 名来自 33206 名患者的 DPR，采集时间为 2006 年 10 月至 2018 年 6 月。匹配过程基于加速稳健特征（SURF）算法，以找到两个 DPR（未知人员和数据库条目）之间的唯一对应点。找到的匹配点的数量是识别的指标。通过与特征数据库的内容进行比较，成功识别了所有 43 个人（100％）。该实验设置旨在使用自动算法系统根据 DPR 识别未知人员。所提出的工具能够过滤具有许多潜在匹配伙伴的大型数据库。即使过去已经移除或添加了牙齿特征，这种识别方法也适用。
