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 criticized in the scientific and legal literature. Instead, scholars have proposed to characterize 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 renders likelihood-based inference impossible. The method used in the current study quantified the weight of fingerprint evidence in forensic science; however, the researchers note that it can be applied to any other forensic pattern evidence. (publisher abstract modified)
A ROC-based Approximate Bayesian Computation Algorithm for Model Selection: Application to Fingerprint Comparisons in Forensic Science
NCJ Number
254288
Journal
arXiv Dated: 2019
Date Published
2019
Length
10 pages
Annotation
This study quantified the weight of fingerprint evidence by using an Approximate Bayesian Computation (ABC) model selection algorithm to quantify the weight of fingerprint evidence, with the ABC algorithm supplemented with the use of a Receiver Operating Characteristic curve to mitigate the effect of dimensionality.
Abstract