1993
@article{Dau1993,
vgclass = {refpap},
author = {John G. Daugman},
title = {High Confidence Visual Recognition of Persons By a Test of Statistical Independence},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {15},
number = {11},
pages = {1148--1161},
year = {1993},
abstract = {A method for rapid visual recognition of personal identity
is described, based on the failure of a statistical test of
independence. The most unique phenotypic feature visible in a person's
face is the detailed texture of each eye's iris: an estimate of its
statistical complexity in a sample of the human population reveals
variation corresponding to several hundred independent
degrees-of-freedom. Morphogenetic randomness in the texture expressed
phenotypically in the iris trabecular meshwork ensures that a test of
statistical independence on two coded patterns originating from
different eyes is passed almost certainly, whereas the same test is
failed almost certainly when the compared codes originate from the same
eye. The visible texture of a person's iris in a real-time video image
is encoded into a compact sequence of multi-scale quadrature 2-D Gabor
wavelet coefficients, whose most-significant bits comprise a 256-byte
``iris code.'' Statistical decision theory generates identification
decisions from Exclusive-OR comparisons of complete iris codes at the
rate of 4,000 per second, including calculation of decision confidence
levels. The distributions observed empirically in such comparisons
imply a theoretical ``cross-over'' error rate of one in 131,000 when a
decision criterion is adopted that would equalize the False Accept and
False Reject error rates. In the typical recognition case, given the
mean observed degree of iris code agreement, the decision confidence
levels correspond formally to a conditional False Accept probability of
one in about \ldots},
}