Defence Science and Technology Group, P.O. Box 1500, Edinburgh, SA 5111, Australia.
Victorian Institute of Forensic Medicine, 65 Kavanagh St., Southbank, VIC 3006, Australia.
Forensic Sci Int. 2024 Aug;361:112108. doi: 10.1016/j.forsciint.2024.112108. Epub 2024 Jun 13.
Mass disaster events can result in high levels of casualties that need to be identified. Whilst disaster victim identification (DVI) relies on primary identifiers of DNA, fingerprints, and dental, these require ante-mortem data that may not exist or be easily obtainable. Facial recognition technology may be able to assist. Automated facial recognition has advanced considerably and access to ante-mortem facial images are readily available. Facial recognition could therefore be used to expedite the DVI process by narrowing down leads before primary identifiers are made available. This research explores the feasibility of using automated facial recognition technology to support DVI. We evaluated the performance of a commercial-off-the-self facial recognition algorithm on post-mortem images (representing images taken after a mass disaster) against ante-mortem images (representing a database that may exist within agencies who hold face databases for identity documents (such as passports or driver's licenses). We explored facial recognition performance for different operational scenarios, with different levels of face image quality, and by cause of death. Our research is the largest facial recognition evaluation of post-mortem and ante-mortem images to date. We demonstrated that facial recognition technology would be valuable for DVI and that the performance varies by image quality and cause of death. We provide recommendations for future research.
大规模灾难事件可能导致大量伤亡，需要进行身份识别。虽然灾难遇难者识别（DVI）依赖于 DNA、指纹和牙齿等主要识别特征，但这些特征需要生前数据，而这些数据可能不存在或难以获得。面部识别技术可能会有所帮助。自动面部识别技术已经取得了很大的进展，并且可以轻松获得生前的面部图像。因此，面部识别可以通过在主要识别特征可用之前缩小线索范围，从而加快 DVI 过程。本研究探讨了使用自动面部识别技术支持 DVI 的可行性。我们评估了商用现成的面部识别算法在死后图像（代表大规模灾难发生后的图像）上的性能，以及在生前图像（代表可能存在于持有身份文件面部数据库的机构的数据库中，如护照或驾驶执照）上的性能。我们探索了不同操作场景、不同面部图像质量和不同死因的面部识别性能。我们的研究是迄今为止对死后和生前图像进行的最大规模的面部识别评估。我们表明，面部识别技术对于 DVI 将非常有价值，并且性能因图像质量和死因而异。我们为未来的研究提供了建议。
