Modern Statistical Models for Forensic Fingerprint Examinations: A Critical Review

Joshua Abraham, Christophe Champod, Chris LENNARD, Claude Roux

    Research output: Contribution to journalArticlepeer-review

    33 Citations (Scopus)


    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
    Original languageEnglish
    Pages (from-to)131-150
    Number of pages20
    JournalForensic Science International
    Issue number1-3
    Publication statusPublished - 2013


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