Modeling Spatial Relationships Between Features When Quantifying the Weight of Fingerprint Evidence

• Activity Number: 19
• Type: Topic Contributed
• Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
• Sponsor: Ad Hoc Advisory Committee on Forensic Science
• Author(s): Cedric Neumann* and Vered Madar and Michael L. Lavine and Jonah K. Amponsah and Christopher Saunders
• Companies: San Diego State University and Statistical and Applied Mathematical Sciences Institute and University of Massachusetts and South Dakota State University and MITRE/South Dakota State University
• Keywords: fingerprint ; shoeprint ; spatial model ; bayes factor ; weight of evidence

Abstract

The quantification of the weight of forensic evidence involves characterizing the probability distribution of complex and heterogeneous vectors representing friction ridge features. Most models proposed in the past attempt to reduce the complexity of the modeling by relying on unverified independence assumptions or on ad-hoc numerical methods. In particular, studies have shown that friction ridge features are not independent and that their spatial relationship is a key contributor to the probative value of a trace recovered at a crime scene. Several models have attempted to account for this spatial relationship, but none has proposed a formal solution to this issue. In this paper, we present two models that formally characterize the probability distribution of spatial relationships between friction ridge features and enable the quantification of the weight of fingerprint evidence. The application of these models can easily be extended to other types of evidence where the spatial characteristics between features is important, such as shoeprint evidence.