Modern statistical models for forensic fingerprint examinations : a critical review

Détails

ID Serval
serval:BIB_3C9C3C5CC992
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Modern statistical models for forensic fingerprint examinations : a critical review
Périodique
Forensic Science International
Auteur(s)
Abraham J., Champod C., Lennard C., Roux C.
ISSN
1872-6283
ISSN-L
0379-0738
Statut éditorial
Publié
Date de publication
10/2013
Peer-reviewed
Oui
Volume
232
Numéro
1-3
Pages
131-150
Langue
anglais
Résumé
Over the last decade, the development of statistical models in support of forensic fingerprint identification has been the subject of increasing research attention, spurned on recently by commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. Such models are increasingly seen as useful tools in support of the fingerprint identification process within or in addition to the ACE-V framework.
This paper provides a critical review of recent statistical models from both a practical and theoretical perspective. This includes analysis of models of two different methodologies: Probability of Random Correspondence (PRC) models that focus on calculating probabilities of the occurrence of fingerprint configurations for a given population, and Likelihood Ratio (LR) models which use analysis of corresponding features of fingerprints to derive a likelihood value representing the evidential weighting for a potential source.
Mots-clé
Statistical models, Fingerprint modelling, Fingerprint evidence, Likelihood Ratios, Review paper
Création de la notice
23/09/2013 13:27
Dernière modification de la notice
20/08/2019 14:32
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