Hierarchical mixture models for assessing fingerprint individuality

TL;DR

This paper introduces hierarchical mixture models with a Bayesian inference approach to quantify fingerprint individuality, capturing population variability and improving the assessment of fingerprint evidence in forensic contexts.

Contribution

It develops a novel hierarchical mixture modeling framework with Bayesian inference for assessing fingerprint individuality, addressing population variability and complexity.

Findings

Hierarchical mixture models effectively represent fingerprint feature variability.

Bayesian inference via reversible jump MCMC estimates model parameters.

Application to NIST data demonstrates practical utility in forensic analysis.

Abstract

The study of fingerprint individuality aims to determine to what extent a fingerprint uniquely identifies an individual. Recent court cases have highlighted the need for measures of fingerprint individuality when a person is identified based on fingerprint evidence. The main challenge in studies of fingerprint individuality is to adequately capture the variability of fingerprint features in a population. In this paper hierarchical mixture models are introduced to infer the extent of individualization. Hierarchical mixtures utilize complementary aspects of mixtures at different levels of the hierarchy. At the first (top) level, a mixture is used to represent homogeneous groups of fingerprints in the population, whereas at the second level, nested mixtures are used as flexible representations of distributions of features from each fingerprint. Inference for hierarchical mixtures is more challenging since the number of unknown mixture components arise in both the first and second levels of the hierarchy. A Bayesian approach based on reversible jump Markov chain Monte Carlo methodology is developed for the inference of all unknown parameters of hierarchical mixtures. The methodology is illustrated on fingerprint images from the NIST database and is used to make inference on fingerprint individuality estimates from this population.