Iris recognition provides real-time, high confidence identification of persons by analysis of the random
patterns that are visible within the iris of an eye from some distance. Because the iris is a protected,
internal, organ whose random texture is epigenetic and stable over the lifespan, it can serve as a
living password. Recognition decisions are made with confidence levels high enough to support rapid
exhaustive searches through national-sized databases. The principle that underlies these algorithms is
the failure of an efficient test of statistical independence involving more than 200 degrees-of-freedom,
based on phase sequencing each iris pattern with quadrature 2D wavelets. Different persons always
pass this test of statistical independence, but images from the same iris almost always fail this
test of independence. Database search speeds are around 1 million persons per second per CPU.
Data from 200 billion cross-comparisons between different eyes will be presented in this talk, using
a database consisting of 632,500 iris images acquired in the United Arab Emirates in a networked
national border-crossing security system which performs, every day, about 9 billion iris comparisons
using these algorithms. Current research efforts with this technology aim to make it more tolerant
of difficult conditions of iris capture, such as "iris on the move," at a distance, and off-axis.