Modern statistical models for forensic fingerprint examinations: a critical review.
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.
Centre for Forensic Science, University of Technology Sydney, Broadway, NSW 2007, Australia. Electronic address: joshua.abraham@uts.edu.au.
Joshua
J
Abraham
Christophe
C
Champod
Chris
C
Lennard
Claude
C
Roux
D003878:Dermatoglyphics; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D011336:Probability
Fingerprint evidence; Fingerprint modelling; Likelihood Ratios; Review paper; Statistical models