Quantification of the weight of fingerprint evidence using a ROC-based Approximate Bayesian Computation algorithm for model selection

TL;DR

This paper introduces a novel, computationally efficient ABC-based method combined with ROC analysis to quantify the evidential weight of fingerprint patterns, addressing the limitations of likelihood-based inference in forensic science.

Contribution

It presents a new ABC algorithm with ROC integration for model selection, enabling quantification of fingerprint evidence support without likelihood calculations.

Findings

The method effectively quantifies fingerprint evidence support.

It mitigates the curse of dimensionality in forensic pattern analysis.

The approach is adaptable to other forensic evidence types.

Abstract

For more than a century, fingerprints have been used with considerable success to identify criminals or verify the identity of individuals. The categorical conclusion scheme used by fingerprint examiners, and more generally the inference process followed by forensic scientists, have been heavily criticised in the scientific and legal literature. Instead, scholars have proposed to characterise the weight of forensic evidence using the Bayes factor as the key element of the inference process. In forensic science, quantifying the magnitude of support is equally as important as determining which model is supported. Unfortunately, the complexity of fingerprint patterns render likelihood-based inference impossible. In this paper, we use an Approximate Bayesian Computation model selection algorithm to quantify the weight of fingerprint evidence. We supplement the ABC algorithm using a Receiver Operating Characteristic curve to mitigate the effect of the curse of dimensionality. Our modified algorithm is computationally efficient and makes it easier to monitor convergence as the number of simulations increase. We use our method to quantify the weight of fingerprint evidence in forensic science, but we note that it can be applied to any other forensic pattern evidence.