Likelihood ratio data to report the validation of a forensic fingerprint evaluation method

Daniel Ramos, Rudolf Haraksim, Didier Meuwly*

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    15 Citations (Scopus)
    218 Downloads (Pure)


    Data to which the authors refer to throughout this article are likelihood ratios (LR) computed from the comparison of 5–12 minutiae fingermarks with fingerprints. These LRs data are used for the validation of a likelihood ratio (LR) method in forensic evidence evaluation. These data present a necessary asset for conducting validation experiments when validating LR methods used in forensic evidence evaluation and set up validation reports. These data can be also used as a baseline for comparing the fingermark evidence in the same minutiae configuration as presented in (D. Meuwly, D. Ramos, R. Haraksim,) [1], although the reader should keep in mind that different feature extraction algorithms and different AFIS systems used may produce different LRs values. Moreover, these data may serve as a reproducibility exercise, in order to train the generation of validation reports of forensic methods, according to [1]. Alongside the data, a justification and motivation for the use of methods is given. These methods calculate LRs from the fingerprint/mark data and are subject to a validation procedure. The choice of using real forensic fingerprint in the validation and simulated data in the development is described and justified. Validation criteria are set for the purpose of validation of the LR methods, which are used to calculate the LR values from the data and the validation report. For privacy and data protection reasons, the original fingerprint/mark images cannot be shared. But these images do not constitute the core data for the validation, contrarily to the LRs that are shared.

    Original languageEnglish
    Pages (from-to)75-92
    Number of pages18
    JournalData in brief
    Publication statusPublished - 1 Feb 2017


    • Accreditation
    • Automatic interpretation method
    • Likelihood ratio data
    • Method validation
    • Strength of evidence
    • Validation report


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