skip navigation


Register for Latest Research

Stay Informed
Register with NCJRS to receive NCJRS's biweekly e-newsletter JUSTINFO and additional periodic emails from NCJRS and the NCJRS federal sponsors that highlight the latest research published or sponsored by the Office of Justice Programs.

NCJRS Abstract

The document referenced below is part of the NCJRS Virtual Library collection. To conduct further searches of the collection, visit the Virtual Library. See the Obtain Documents page for direction on how to access resources online, via mail, through interlibrary loans, or in a local library.


NCJ Number: 230166 Add to Shopping cart Find in a Library
Title: Adding Human Expertise to the Quantitative Analysis of Fingerprints, Final Report
Author(s): Thomas Busey; Chen Yu
Date Published: 2010
Page Count: 53
Sponsoring Agency: National Institute of Justice (NIJ)
Washington, DC 20531
National Institute of Justice/NCJRS
Rockville, MD 20849
NCJRS Photocopy Services
Rockville, MD 20849-6000
Grant Number: 2005-MU-BX-K076
Sale Source: National Institute of Justice/NCJRS
Box 6000
Rockville, MD 20849
United States of America

NCJRS Photocopy Services
Box 6000
Rockville, MD 20849-6000
United States of America
Document: PDF
Type: Report (Study/Research)
Format: Document
Language: English
Country: United States of America
Annotation: The goal of this project is to characterize the nature of human expertise using eye tracking methodologies, and then use these results to develop and refine quantitative metrics of the information contained in friction ridge patterns.
Abstract: Current quantitative approaches to fingerprint matching and analysis are not based on human data and therefore do not take advantage of the full capabilities of the human visual system. Since humans routinely outperform automated fingerprint recognition systems, it is clear that quantitative approaches can be improved by adopting some of the strategies that humans employ; however, humans often have difficulty describing the result of perceptual processing, and may not even know what information they are using. To address this deficit, the authors used eye tracking to identify what information human experts rely on. They constructed a portable eye tracking system that enabled them to collect data from experts and novices while they perform tasks similar to latent print examinations. Once they analyzed the data, they obtained a record of the regions visited by the experts as they compared pairs of fingerprints. The authors then developed a series of computational analyses to identify the nature of the expertise. This took the form of data reduction procedures on pixel crops from the fingerprint images, as well as the development of candidate information metrics that the data from experts helps validate. The results demonstrate clearly that human expertise can be inferred from eye gaze information through a process of carefully designed studies and hypothesis testing of candidate information metrics. Because the authors’ candidate metrics take the form of mathematical and computational models, they are readily applicable to machine comparison approaches, and also can be used to identify the diagnosticity and rarity of particular features in novel prints. Appendix, figures, and references (Author Abstract)
Main Term(s): Criminology
Index Term(s): Automated fingerprint processing; Comparative analysis; Fingerprints; Investigative techniques; Latent fingerprints; Mathematical modeling; NIJ final report
To cite this abstract, use the following link:

*A link to the full-text document is provided whenever possible. For documents not available online, a link to the publisher's website is provided. Tell us how you use the NCJRS Library and Abstracts Database - send us your feedback.