Probabilistic graphical models to deal with age estimation of living persons

Sironi, Emanuele, Gallidabino, Matteo, Weyermann, Céline and Taroni, Franco (2016) Probabilistic graphical models to deal with age estimation of living persons. International Journal of Legal Medicine, 130 (2). pp. 475-488. ISSN 0937-9827

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Official URL: http://dx.doi.org/10.1007/s00414-015-1173-7

Abstract

Due to the rise of criminal, civil and administrative judicial situations involving people lacking valid identity documents, age estimation of living persons has become an important operational procedure for numerous forensic and medicolegal services worldwide. The chronological age of a given person is generally estimated from the observed degree of maturity of some selected physical attributes by means of statistical methods. However, their application in the forensic framework suffers from some conceptual and practical drawbacks, as recently claimed in the specialised literature. The aim of this paper is therefore to offer an alternative solution for overcoming these limits, by reiterating the utility of a probabilistic Bayesian approach for age estimation. This approach allows one to deal in a transparent way with the uncertainty surrounding the age estimation process and to produce all the relevant information in the form of posterior probability distribution about the chronological age of the person under investigation. Furthermore, this probability distribution can also be used for evaluating in a coherent way the possibility that the examined individual is younger or older than a given legal age threshold having a particular legal interest. The main novelty introduced by this work is the development of a probabilistic graphical model, i.e. a Bayesian network, for dealing with the problem at hand. The use of this kind of probabilistic tool can significantly facilitate the application of the proposed methodology: examples are presented based on data related to the ossification status of the medial clavicular epiphysis. The reliability and the advantages of this probabilistic tool are presented and discussed.

Item Type: Article
Uncontrolled Keywords: Forensic medicine, Age estimation, Forensic interpretation, Probabilistic approach, Bayesian networks
Subjects: F400 Forensic and Archaeological Science
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Becky Skoyles
Date Deposited: 21 Sep 2018 10:37
Last Modified: 11 Oct 2019 08:21
URI: http://nrl.northumbria.ac.uk/id/eprint/35858

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