Assessing Fingerprint Individuality Using EPIC: A Case Study in the Analysis of Spatially Dependent Marked Processes

Fingerprint individuality refers to the extent of uniqueness of fingerprints and is governed by the distribution of fingerprint features, termed minutiae, in a population. This paper develops a flexible class of marked point processes for minutiae and associated methodology for assessing fingerprint individuality. Inference is carried out in a Bayesian MCMC framework. The flexibility of the model fit to different kinds of minutiae patterns is demonstrated using real fingerprints. Evidence of a Paired Impostor Correspondence (EPIC) is developed as a measure of fingerprint individuality and its value is obtained using a simulation procedure based on the fitted models.