An AFIS candidate list centric fingerprint likelihood ratio model based on morphometric and spatial analyses (MSA)

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.