Modern statistical models for forensic fingerprint examinations : a critical review

Details

Serval ID
serval:BIB_3C9C3C5CC992
Type
Article: article from journal or magazin.
Collection
Publications
Title
Modern statistical models for forensic fingerprint examinations : a critical review
Journal
Forensic Science International
Author(s)
Abraham J., Champod C., Lennard C., Roux C.
ISSN
1872-6283
ISSN-L
0379-0738
Publication state
Published
Issued date
10/2013
Peer-reviewed
Oui
Volume
232
Number
1-3
Pages
131-150
Language
english
Abstract
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.
Keywords
Statistical models, Fingerprint modelling, Fingerprint evidence, Likelihood Ratios, Review paper
Create date
23/09/2013 12:27
Last modification date
20/08/2019 13:32
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