Biometrics, the science of using physical traits to identify
individuals, is playing an increasing role in our security-conscious
society and across the globe. Biometric authentication, or
bioauthentication, systems are being used to secure everything from
amusement parks to bank accounts to military installations. Yet
developments in this field have not been matched by an equivalent
improvement in the statistical methods for evaluating these systems.
Compensating for this need, this unique text/reference provides a basic
statistical methodology for practitioners and testers of
bioauthentication devices, supplying a set of rigorous statistical
methods for evaluating biometric authentication systems. This framework
of methods can be extended and generalized for a wide range of
applications and tests. This is the first single resource on statistical
methods for estimation and comparison of the performance of biometric
authentication systems. The book focuses on six common performance
metrics: for each metric, statistical methods are derived for a single
system that incorporates confidence intervals, hypothesis tests, sample
size calculations, power calculations and prediction intervals. These
methods are also extended to allow for the statistical comparison and
evaluation of multiple systems for both independent and paired data.
Topics and features: * Provides a statistical methodology for the most
common biometric performance metrics: failure to enroll (FTE), failure
to acquire (FTA), false non-match rate (FNMR), false match rate (FMR),
and receiver operating characteristic (ROC) curves * Presents methods
for the comparison of two or more biometric performance metrics *
Introduces a new bootstrap methodology for FMR and ROC curve estimation
* Supplies more than 120 examples, using publicly available biometric
data where possible * Discusses the addition of prediction intervals to
the bioauthentication statistical toolset * Describes sample-size and
power calculations for FTE, FTA, FNMR and FMR Researchers, managers and
decisions makers needing to compare biometric systems across a variety
of metrics will find within this reference an invaluable set of
statistical tools. Written for an upper-level undergraduate or master's
level audience with a quantitative background, readers are also expected
to have an understanding of the topics in a typical undergraduate
statistics course. Dr. Michael E. Schuckers is Associate Professor of
Statistics at St. Lawrence University, Canton, NY, and a member of the
Center for Identification Technology Research.
