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For more than a century, fingerprints have been used with considerablesuccess to identify criminals or verify the identity of individuals. Thecategorical conclusion scheme used by fingerprint examiners, and more generallythe inference process followed by forensic scientists, have been heavilycriticised in the scientific and legal literature. Instead, scholars haveproposed to characterise the weight of forensic evidence using the Bayes factoras the key element of the inference process. In forensic science, quantifyingthe magnitude of support is equally as important as determining which model issupported. Unfortunately, the complexity of fingerprint patterns renderlikelihood-based inference impossible. In this paper, we use an ApproximateBayesian Computation model selection algorithm to quantify the weight offingerprint evidence. We supplement the ABC algorithm using a ReceiverOperating Characteristic curve to mitigate the effect of the curse ofdimensionality. Our modified algorithm is computationally efficient and makesit easier to monitor convergence as the number of simulations increase. We useour 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.

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