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Probabilistic graphical models to deal with age estimation of living persons

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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.

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Notes

  1. The English version of the Swiss Civil code can be read at the following internet address: http://www.admin.ch/ch/e/rs/2/210.en.pdf (last access: 04th of February 2015).

  2. A free demonstrative version of this software—Hugin Lite—is available on the website www.hugin.com.

  3. R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Wien, Austria. URL: http://www.R-project.org/.

  4. The expression used to compile the CPT of the node “C = si” is the following in the Hugin language: Distribution (1/(1 + exp (−(18.39 − 1.35 × A))), 1/(1 + exp (−(21.12 − 1.17 × A))) × (1 − 1/(1 + exp (−(18.39 − 1.35 × A)))), 1/(1 + exp (−(30.72 − 1.30 × A))) × (1 − 1 / (1 + exp (−(18.39 − 1.35 × A)))) × (1 − 1/(1 + exp (−(21.12 − 1.17 × A)))), (1 − 1/(1 + exp (−(30.72 − 1.30 × A)))) × (1 − 1/(1 + exp (−(18.39 − 1.35 × A)))) × (1 − 1/(1 + exp (−(21.12 − 1.17 × A))))).

  5. The expression used for the node “C = si” became: Distribution (1/(1 + exp (−(α 1 − β 1 × A))), 1/(1 + exp (−(α 2 − β 2 × A))) × (1 − 1/(1 + exp (−(α 1 − β 1 × A)))), 1/(1 + exp (−(α 3 − β 3 × A))) × (1 − 1 / (1 + exp (−(α1 − β 1 × A)))) × (1 − 1 / (1 + exp (−(α 2 − β 2 × A)))), (1 − 1 / (1 + exp (−(α 3 − β 3 × A)))) × (1 − 1 / (1 + exp (−(α 1 − β 1 × A)))) × (1 − 1 / (1 + exp (−(α 2 − β2 × A))))).

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Acknowledgments

The authors wish to thank the collaborators from the Centre Universitaire Romand de Médecine Légale (CURML) for useful advices and the Swiss National Foundation for supporting a small part of this research (grant no. PP00P1_123358). The authors are also grateful to the Editor and the anonymous reviewers for their fruitful comments.

Conflict of interest

The authors declare they have no conflict of interest.

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  1. School of Criminal Justice, University of Lausanne, Building Batochime, 1015, Lausanne, Dorigny, Switzerland

    Emanuele Sironi, Matteo Gallidabino, Céline Weyermann & Franco Taroni

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  1. Emanuele Sironi
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Correspondence to Emanuele Sironi.

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Sironi, E., Gallidabino, M., Weyermann, C. et al. Probabilistic graphical models to deal with age estimation of living persons. Int J Legal Med 130, 475–488 (2016). https://doi.org/10.1007/s00414-015-1173-7

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