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NCJ Number: NCJ 240590   Add to Shopping cart   Find in a Library
Title: Application of Spatial Statistics to Latent Print Identifications: Towards Improved Forensic Science Methodologies
Author(s): Stephen J. Taylor ; Emma K. Dutton ; Patrick R. Aldrich ; Bryan E. Dutton
Date Published: 11/2012
Page Count: 109
Sponsoring Agency: National Institute of Justice
US Department of Justice
Office of Justice Programs
United States of America
Grant Number: 2009-DN-BX-K228
Sale Source: NCJRS Photocopy Services
Box 6000
Rockville, MD 20849-6000
United States of America
Document: PDF 
Type: Research (Applied/Empirical)
Language: English
Country: United States of America
Annotation: This final report presents the results of a study that investigated the question of fingerprint uniqueness by statistically evaluating the spatial distribution of fingerprint ridgeline features.
Abstract: The objectives of this study were twofold: to spatially analyze fingerprint features using geographic information systems (GIS) techniques, and to develop probabilities to provide a quantitative measure of the discriminating value of fingerprint ridgeline features. The parameters of the fingerprint ridgeline features included minutiae location, direction, and minutiae ridgeline configurations. Data for the study were obtained from a dataset of digitized fingerprints from the population in Oregon that were spatially analyzed using GIS software. Results from the GIS-based spatial characterization part of the study include the following: GIS techniques can be used to spatially analyze fingerprint patterns; a variety of analytical tools can be developed to characterize fingerprint features and statistically characterize distributions between pattern types; and minutiae and ridgelines were found to be most densely packed in the region below the core, with the greatest ridgeline-length density surrounding the core. Geometric morphometric analyses were also used in the research to study the shape variations of four fingerprint pattern types with the intent to determine the extent and degree of variation within and among four fingerprint pattern types. The results of these analyses are discussed in the report. The third part of the research used Monte Carlo simulations to calculate random-match probabilities to evaluate the spatial configurations of minutiae within and between pattern types. Implications for policy and practice are discussed. Tables, figures, references, and appendixes
Main Term(s): Fingerprint classification
Index Term(s): Automated fingerprint processing ; Latent fingerprints ; Fingerprint image quality ; Fingerprinting regulations ; Fingerprint detection techniques ; Geographic information systems (GIS) ; NIJ grant-related documents
   
  To cite this abstract, use the following link:
https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=262670

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