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

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 (ABC) model selection algorithm to quantify the weight of fingerprint evidence. We supplement the original ABC model selection algorithm using a Receiver Operating Characteristic curve to mitigate its known shortcomings with respect to the choice of a suitable threshold and the curse of dimensionality. In a sense, we offer an alternative to other methods that have tried to address the same issues. Our method is straightforward to implement, computationally efficient, and visually intuitive for lay individuals (i.e., jurors). We apply 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.

Ensemble learning for score likelihood ratios under the common source problem

“…Although the SLR framework has shown promise in different areas of forensics (handwriting: [8,13,21,25], glass: [40,41], fingerprints: [20,28,35], speaker recognition: [19], ink: [36], MDMA tablets: [3,4], digital: [17], cameras: [42]), concerns have been raised regarding their behavior and use in forensic settings [32,34], and their evaluation has been the subject of extensive research in the literature [18,33]. Ishihara and Carne [22] summarize the benefits and shortcomings of using score-based methods; using a lower dimensional metric reduces the need for complex models, and simpler estimation procedures are required to estimate univariate conditional scores.…”

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

“…1228-1262, and include a specific acknowledgment statement for the source of these images. 6 from Hendricks, Neumann and Saunders (2021). Left panel: Fingermark (top) and control print (bottom), both originating from the same source.…”

“…Fingermark and control print images and annotation data were provided by the Latent Print Unit of the Denver Police Department, Crime Laboratory. 7 from Hendricks, Neumann and Saunders (2021). Left panel: Fingermark (top) and control print (bottom) originating from the different sources.…”

“…Fig 3. Reproduction of Figure7fromHendricks, Neumann and Saunders (2021).Left panel: Fingermark (top) and control print (bottom) originating from the different sources. Potentially corresponding features between the two impressions have been annotated in yellow.…”