DocumentCode :
1163466
Title :
A Parametric Correlation Framework for the Statistical Evaluation and Estimation of Biometric-Based Classification Performance in a Single Environment
Author :
Schuckers, Michael E.
Author_Institution :
Center for Identification Technol. Res., St. Lawrence Univ., Canton, NY
Volume :
4
Issue :
2
fYear :
2009
fDate :
6/1/2009 12:00:00 AM
Firstpage :
231
Lastpage :
241
Abstract :
In this paper, we propose parametric correlation models for the assessment of biometric classification error rates. Correctly specified correlations are integral to variance estimation and the corresponding inferential quantities which depend upon these estimates. We present methodology here for false match and false nonmatch error rates for a single environment. This paper generalizes other work that has previously appeared in the bioauthentication literature. Since symmetric- and asymmetric-matching algorithms are used in practice, we present a general correlation structure for both types of algorithms. Along with the correlation structure, we describe estimators for the parameters in these models. The correlation structure described here for binary decision data is then used to derive explicit confidence intervals and sample-size calculations for the estimation of false match and false nonmatch error rates. We then apply the correlation structure described herein to two match scores databases to illustrate our approach. A discussion of the utility and consequences of this correlation structure are also provided.
Keywords :
binary decision diagrams; biometrics (access control); correlation methods; security of data; asymmetric-matching algorithms; binary decision data; bioauthentication; biometric-based classification performance; false match error rates; false nonmatch error rates; parametric correlation framework; parametric correlation models; statistical evaluation; symmetric-matching algorithms; variance estimation; Biometric authentication; confidence intervals; effective sample size; false accept rate; false match rate (FMR); false nonmatch rate (FNMR); false reject rate; sample-size calculations; variance structure;
fLanguage :
English
Journal_Title :
Information Forensics and Security, IEEE Transactions on
Publisher :
ieee
ISSN :
1556-6013
Type :
jour
DOI :
10.1109/TIFS.2008.2012206
Filename :
4785113
Link To Document :
بازگشت