In this paper, we present a central limit theorem (CLT) for the estimation of a false match rate for a single
matching system. The false match rate is often a significant factor in an evaluation of such a matching system. To
achieve the main result here we utilize the covariance/correlation structure for matching proposed by Schuckers.
Along with the main result we present an illustration of the methodology here on biometric authentication data
from Ross and Jain. This illustration is from resampling match decisions on three different biometric modalities:
hand geometry, fingerprint and facial recognition and shows that as the number of matching pairs grows the
sampling distribution for an FMR approaches a Gaussian distribution. These results suggest that statistical
inference for a FMR based upon a Gaussian distribution is appropriate.
A change in classification error rates for a biometric device is often referred to as template aging. Here we offer two methods for determining whether the effect of time is statistically significant. The first of these is the use of a generalized linear model to determine if these error rates change linearly over time. This approach generalizes previous work assessing the impact of covariates using generalized linear models. The second approach uses of likelihood ratio tests methodology. The focus here is on statistical methods for estimation not the underlying cause of the change in error rates over time. These methodologies are applied to data from the National Institutes of Standards and Technology Biometric Score Set Release 1. The results of these applications are discussed.
Confidence intervals are an important way to assess and estimate a
parameter. In the case of biometric identification devices,
several approaches to confidence intervals for an error rate have
been proposed. Here we evaluate six of these methods. To complete
this evaluation, we simulate data from a wide variety of parameter
values. This data are simulated via a correlated binary
distribution. We then determine how well these methods do at what
they say they do: capturing the parameter inside the confidence
interval. In addition, the average widths of the various
confidence intervals are recorded for each set of parameters. The
complete results of this simulation are presented graphically for
easy comparison. We conclude by making a recommendation
regarding which method performs best.
Conference Committee Involvement (6)
Biometric Technology for Human Identification IX
23 April 2012 | Baltimore, Maryland, United States
Biometric Technology for Human Identification VIII
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