An Analysis of 1-to-First Matching in Iris Recognition

Andrey Kuehlkamp, Kevin Bowyer

Abstract:

Iris recognition systems are a mature technology that is
widely used throughout the world. In identification (as op-
posed to verification) mode, an iris to be recognized is typ-
ically matched against all N enrolled irises. This is the
classic ”1-to-N search”. In order to improve the speed
of large-scale identification, a modified ”1-to-First” search
has been used in some operational systems. A 1-to-First
search terminates with the first below-threshold match that
is found, whereas a 1-to-N search always finds the best
match across all enrollments. We know of no previous stud-
ies that evaluate how the accuracy of 1-to-First search dif-
fers from that of 1-to-N search. Using a dataset of over
50,000 iris images from 2,800 different irises, we perform
experiments to evaluate the relative accuracy of 1-to-First
and 1-to-N search. We evaluate how the accuracy differ-
ence changes with larger numbers of enrolled irises, and
with larger ranges of rotational difference allowed between
iris images. We find that False Match error rate for 1-to-
First is higher than for 1-to-N, and the the difference grows
with larger number of enrolled irises and with larger range
of rotation.