Наредни састанак Семинара биће одржан онлајн у среду, 15. новембра 2023. са почетком у 19 часова.
Предавач: Professor Oscar Cordón, Dept. of Computer Science and Artificial Intelligence, Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada
Наслов предавања: ARTIFICIAL INTELLIGENCE APPLICATIONS FOR FORENSIC ANTHROPOLOGY AND HUMAN IDENTIFICATION
Апстракт: Skeleton-based forensic identification methods carried out by anthropologists, odontologists, and pathologists represent the first step in every human identification (ID) process and the victim’s last chance for identification when DNA or fingerprints cannot be applied. They include methods as biological profiling (BP), comparative radiography (CR), craniofacial superimposition (CFS), and comparison of dental records. BP involves the study of skeletal remains to find characteristic traits (age, sex, stature, and ancestry) that support determining the identity of the individual. It plays a crucial role in narrowing the range of potential matches during the process of ID, prior to the corroboration by any ID technique. CR considers the ante-mortem (AM) and post-mortem (PM) comparison of different bones and cavities (skull frontal sinuses, clavicles, patellae, …) which have been reported as useful for positive identification based on their individuality and uniqueness. CFS aims to overlay a skull with some AM images of a candidate to determine if they correspond to the same person.
However, practitioners still follow an observational paradigm using subjective methods introduced many decades ago; namely, oral description and written documentation of the findings obtained and the manual and visual comparison of AM and PM data. Designing systematic, automatic and trustworthy methods to support the forensic anthropologist when applying BP, CFS and CR, avoiding the use of subjective, error-prone, and time-consuming manual procedures, is mandatory to enhance forensic ID. The use of artificial intelligence (specifically evolutionary algorithms, fuzzy sets, and deep learning), computer vision (3D-2D image registration and image processing), and explainable machine learning is a natural way to achieve this aim. This plenary talk is devoted to present three intelligent systems for CFS, skeleton-based age-at-death assessment, and sex assessment developed in collaboration with the University of Granada’s Physical Anthropology Lab within an eighteen year long research project. One of those systems is protected by an international patent, exploited by Panacea Cooperative Research, and is under commercialization in different countries.
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