A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification
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A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human IdentificationDate
2002-07-08Citation
Mesejo, P.; Martos, R.; Ibáñez, Ó.; Novo, J.; Ortega, M. A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification. Appl. Sci. 2020, 10, 4703.
Abstract
[Abstract]
This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.
Keywords
Forensic medicine
Forensic anthropology
Forensic imaging
Skeleton-based forensic identification
Machine learning
Computer vision
Soft computing
Biological profiling
Comparative radiography
Craniofacial identification
Forensic anthropology
Forensic imaging
Skeleton-based forensic identification
Machine learning
Computer vision
Soft computing
Biological profiling
Comparative radiography
Craniofacial identification
Editor version
Rights
Atribución 4.0 España
ISSN
2076-3417
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