Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Nonparametric Analysis of Fingerprint Data on Large Data Sets

Published

Author(s)

Jin Chu Wu, Charles Wilson

Abstract

By executing different fingerprint-image matching algorithms on large data sets, it reveals that the match and non-match similarity scores have no specific underlying distribution function. Thus, it requires a nonparametric analysis for fingerprint-image matching algorithms on large data sets without any assumption about such irregularly discrete distribution functions. A precise receiver operating characteristic (ROC) curve based on the true accept rate (TAR) of the match similarity scores and the false accept rate (FAR) of the non-match similarity scores can be constructed. The area under such an ROC curve computed using the trapezoidal rule is equivalent to the Mann Whitney statistic directly formed from the match and non-match similarity scores. Thereafter, the Z statistic formulated using the areas under ROC curves along with their variances and the correlation coefficient is applied to test the significance of the difference between two ROC curves. Four examples from the extensive testing of commercial fingerprint systems at the National Institute of Standards and Technology are provided. The nonparametric approach presented in this article can also be employed in the analysis of other large biometric data sets.
Citation
Pattern Recognition
Issue
40

Keywords

Fingerprint matching, Nonparametric analysis, Receiver operating characteristic (ROC) curve, Mann Whitney statistic, Significance test

Citation

, J. and Wilson, C. (2006), Nonparametric Analysis of Fingerprint Data on Large Data Sets, Pattern Recognition, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=152121 (Accessed April 22, 2024)
Created December 7, 2006, Updated February 19, 2017