  Although temporal persistence, or permanence, is a well understood
requirement for optimal biometric features, there is no general agreement on
how to assess temporal persistence. We suggest that the best way to assess
temporal persistence is to perform a test-retest study, and assess test-retest
reliability. For ratio-scale features that are normally distributed, this is
best done using the Intraclass Correlation Coefficient (ICC). For 10 distinct
data sets (8 eye-movement related, and 2 gait related), we calculated the
test-retest reliability ('Temporal persistence') of each feature, and compared
biometric performance of high-ICC features to lower ICC features, and to the
set of all features. We demonstrate that using a subset of only high-ICC
features produced superior Rank-1-Identification Rate (Rank-1-IR) performance
in 9 of 10 databases (p = 0.01, one-tailed). For Equal Error Rate (EER), using
a subset of only high-ICC features produced superior performance in 8 of 10
databases (p = 0.055, one-tailed). In general, then, prescreening potential
biometric features, and choosing only highly reliable features will yield
better performance than lower ICC features or than the set of all features
combined. We hypothesize that this would likely be the case for any biometric
modality where the features can be expressed as quantitative values on an
interval or ratio scale, assuming an adequate number of relatively independent
features.
