  Many developers of biometric systems start with modest samples before general
deployment. They are interested in how their systems will work with much larger
samples. We evaluated the effect of gallery size on biometric performance.
Identification rates describe the performance of biometric identification,
whereas ROC-based measures describe the performance of biometric authentication
(verification). Therefore, we examined how increases in gallery size affected
identification rates (i.e., Rank-1 Identification Rate, or Rank-1 IR) and
ROC-based measures such as equal error rate (EER). We studied these phenomena
with synthetic data as well as real data from a face recognition study. It is
well known that the Rank-1 IR declines with increasing gallery size. We have
provided further insight into this decline. We have shown that this
relationship is linear in log(Gallery Size). We have also shown that this
decline can be counteracted with the inclusion of additional information
(features) for larger gallery sizes. We have also described the curves which
can be used to predict how much additional information is required to stabilize
the Rank-1 IR as a function of gallery size. These equations are also linear in
log(gallery size). We have also shown that the entire ROC curve is not
systematically affected by gallery size, and so ROC-based scalar performance
metrics such as EER are also stable across gallery size.
