Application of Spatial Statistics to Latent Print Identifications: Towards Improved Forensic Science Methodologies.

摘要

In 2010 we initiated a research project to address criticisms raised in a 2009 National Academy of Sciences (NAS) report regarding the presumption of fingerprint uniqueness and the reliability of latent print identifications using the ACE-V methodology (National Research Council 2009). This project addresses the question of fingerprint uniqueness (i.e., the discriminating value of the various fingerprint ridgeline features) by statistically evaluating the spatial distribution of these features. The purpose of the project was to review the latent print ACE-V comparison methodology to ascertain the fingerprint features considered during the comparison process and apply principles of spatial analyses to calculate false-match probabilities. The objectives were to spatially analyze fingerprint features (e.g., minutiae and ridge lines) using Geographic Information Systems (GIS) techniques and empirically derive probabilities to provide a quantitative measure of the discriminating value of the various ridgeline features. The resultant probabilities are applicable for subsequent qualification of latent print comparison conclusions. Project methods included spatial pattern characterization using GIS, geometric morphometric (GM) analysis, and the calculation of false-match probabilities using Monte Carlo (MC) simulations. A data set of digitized fingerprints from the Oregon population was compiled and spatially analyzed utilizing GIS software to place minutiae and ridge line features in a common Cartesian coordinate system. The parameters of these fingerprint features, including minutiae location, direction and minutiae ridgeline configurations, were evaluated. Geometric morphometrics was used to study shape variation between and among fingerprint pattern types. GIS-based procedures were established for the selection of landmarks and semi-landmarks, the superimposition of fingerprint images, the visualization of shape change, the ordination of superimposition data, and the application of multivariate statistics. Using MC simulations, random-match probabilities were calculated to evaluate the spatial configurations of minutiae within and between pattern types to quantitatively evaluate the discriminating value of fingerprints features; that is, do two fingerprints or two regions of different fingerprints have the same spatial distribution of minutiae and ridgelines. MC simulations were performed using 3, 5, 7 and 9 minutiae with other minutiae attributes chosen for additional match criteria.