Spatial analysis of corresponding fingerprint features from match and close non-match populations

Joshua Abraham, Christophe Champod, Chris LENNARD, Claude Roux

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)


    The development of statistical models for forensic fingerprint identification purposes has been the subject of increasing research attention in recent years. This can be partly seen as a response to a number of commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. In addition, key forensic identification bodies such as ENFSI [1] and IAI [2] have recently endorsed and acknowledged the potential benefits of using statistical models as an important tool in support of the fingerprint identification process within the ACE-V framework.

    In this paper, we introduce a new Likelihood Ratio (LR) model based on Support Vector Machines (SVMs) trained with features discovered via morphometric and spatial analyses of corresponding minutiae configurations for both match and close non-match populations often found in AFIS candidate lists. Computed LR values are derived from a probabilistic framework based on SVMs that discover the intrinsic spatial differences of match and close non-match populations. Lastly, experimentation performed on a set of over 120,000 publicly available fingerprint images (mostly sourced from the National Institute of Standards and Technology (NIST) datasets) and a distortion set of approximately 40,000 images, is presented, illustrating that the proposed LR model is reliably guiding towards the right proposition in the identification assessment of match and close non-match populations. Results further indicate that the proposed model is a promising tool for fingerprint practitioners to use for analysing the spatial consistency of corresponding minutiae configurations
    Original languageEnglish
    Pages (from-to)87-98
    Number of pages12
    JournalForensic Science International
    Issue number1-3
    Publication statusPublished - 2013


    Dive into the research topics of 'Spatial analysis of corresponding fingerprint features from match and close non-match populations'. Together they form a unique fingerprint.

    Cite this