An AFIS Candidate List Centric Fingerprint Likelihood Ratio Model based on Morphometric and Spatial Analyses (MSA)

Joshua Abraham, Paul Kwan, Christophe Champod, Christopher Lennard, Claude Roux

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Abstract

In this chapter, we will firstly discuss the current state of forensic fingerprint identification and how models play an important role for the future, followed by a brief introduction and review into relevant statistical models. Next, we will introduce a Likelihood Ratio (LR) model based on Support Vector Machines (SVMs) trained with features discovered via the morphometric and other spatial analyses of matching minutiae for both genuine and close imposter (or match and close non-match) populations typically recovered from Automated Fingerprint Identification System (AFIS) candidate lists. Lastly, experimentation performed on a set of over 60,000 publicly available fingerprint images (mostly sourced from NIST and FVC databases) and a distortion set of 6,000 images will be presented, illustrating that the proposed LR model is reliably guiding towards the right proposition in the identification assessment for both genuine and high ranking imposter populations, based on the discovered distortion characteristic differences of each population.
Original languageEnglish
Title of host publicationNew Trends and Developments in Biometrics
EditorsJucheng Yang, Shan Juan Xie
Place of PublicationUnited States
PublisherIn-Tech
Pages221-250
Number of pages30
Edition1st
ISBN (Print)9789535108597
Publication statusPublished - 2012

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    Abraham, J., Kwan, P., Champod, C., Lennard, C., & Roux, C. (2012). An AFIS Candidate List Centric Fingerprint Likelihood Ratio Model based on Morphometric and Spatial Analyses (MSA). In J. Yang, & S. J. Xie (Eds.), New Trends and Developments in Biometrics (1st ed., pp. 221-250). In-Tech. http://dx.doi.org/10.5772/51184