A. Valsecchi, Ó. Gómez, A. González, M. Macias, M. de Dios, M. Panizo, K. Prada, M. Flores, S. Kaiser, N. Lurromi, E. Bermejo, P. Mesejo, S. Damas, Ó. Cordón, Ó. Ibáñez
{"title":"Skeleton-ID: AI-driven Human Identification","authors":"A. Valsecchi, Ó. Gómez, A. González, M. Macias, M. de Dios, M. Panizo, K. Prada, M. Flores, S. Kaiser, N. Lurromi, E. Bermejo, P. Mesejo, S. Damas, Ó. Cordón, Ó. Ibáñez","doi":"10.1109/cai54212.2023.00124","DOIUrl":null,"url":null,"abstract":"Victims of crime, migration, natural disasters, and armed conflicts often remain unidentified due to the absence of DNA samples and fingerprints. Skeleton-based identification (ID) methods could help to identify these victims because they are suitable for poorly preserved bodies and require comparison data that is relatively easy to obtain —e.g., a simple photo of the victim— instead of a DNA sample of a close relative or access to a database in another country. Skeleton-ID™ is a game-changing software designed for skeleton-based human identification using artificial intelligence (AI). Combining forensic anthropology and odontology methods with cutting-edge image processing and AI technologies allows us to apply these methods on a large scale. This leads to very high reliability (up to 99%), obtaining explainable results, and a drastic reduction of identification time (days to minutes). The software is accessible through a web browser and can process ante-mortem (AM) information on missing persons as well as post-mortem (PM) information. Usable data includes ordinary photos, 3D scans of bones, x-rays, and dental records.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Conference on Artificial Intelligence (CAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/cai54212.2023.00124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Victims of crime, migration, natural disasters, and armed conflicts often remain unidentified due to the absence of DNA samples and fingerprints. Skeleton-based identification (ID) methods could help to identify these victims because they are suitable for poorly preserved bodies and require comparison data that is relatively easy to obtain —e.g., a simple photo of the victim— instead of a DNA sample of a close relative or access to a database in another country. Skeleton-ID™ is a game-changing software designed for skeleton-based human identification using artificial intelligence (AI). Combining forensic anthropology and odontology methods with cutting-edge image processing and AI technologies allows us to apply these methods on a large scale. This leads to very high reliability (up to 99%), obtaining explainable results, and a drastic reduction of identification time (days to minutes). The software is accessible through a web browser and can process ante-mortem (AM) information on missing persons as well as post-mortem (PM) information. Usable data includes ordinary photos, 3D scans of bones, x-rays, and dental records.