Quantifying the strength of evidence in forensic fingerprints

Thesis

Part I presents a model for fingerprint matching using Bayesian alignment on unlabelled point sets. An efficient Monte Carlo algorithm is developed to calculate the marginal likelihood ratio between the hypothesis that an observed fingerprint and fingermark pair originate from the same finger and the hypothesis that they originate from different fingers. The model achieves good performance on the NIST-FBI fingerprint database of 258 matched fingerprint pairs, though the computed likelihood ratios are implausibly extreme due to oversimplification in our model.

Part II moves to a more theoretical study of proper scoring rules. The chapters in this section are designed to be independent of each other. Chapter 9 uses proper scoring rules to calibrate the implausible likelihood ratios computed in Part I. Chapter 10 defines the class of compatible weighted proper scoring rules. Chapter 11 derives new results for the score matching estimator, which can quickly generate point estimates for a parametric model even when the normalization constant of the distribution is intractable. It is used to find an initial value for the iterative maximization procedure in §3.3.

Appendix A describes a novel algorithm to efficiently sample from the posterior of a von Mises distribution. It is used within the fingerprint model sampling procedure described in §5.6. Appendix B includes various technical results which would otherwise disrupt the flow of the main dissertation.

Authors: Forbes, P

• Publication date: 2014
• DOI: 10.5287/ora-jvvozo480
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: Oxford University, UK
• Language: English
• Keywords: marked point process; weight of evidence; bayesian alignment; likelihood ratio; forensic identification
• Subjects: Statistics