Information Theory and the IrisCode
Authors
Daugman, John
Publication Date
2015-11-12Journal Title
IEEE Transactions on Information Forensics and Security
ISSN
1556-6013
Publisher
IEEE
Volume
11
Pages
400-409
Language
English
Type
Article
Metadata
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Daugman, J. (2015). Information Theory and the IrisCode. IEEE Transactions on Information Forensics and Security, 11 400-409. https://doi.org/10.1109/TIFS.2015.2500196
Abstract
Iris recognition has legendary resistance to False
Matches, and the tools of information theory can help to explain
why. The concept of entropy is fundamental to understanding
biometric collision avoidance. This paper analyses the bit sequences
of IrisCodes computed both from real iris images and
from synthetic “white noise” iris images whose pixel values are
random and uncorrelated. The capacity of the IrisCode as a
channel is found to be 0.566 bits per bit encoded, of which
0.469 bits of entropy per bit is encoded from natural iris images.
The difference between these two rates reflects the existence of
anatomical correlations within a natural iris, and the remaining
gap from one full bit of entropy per bit encoded reflects the
correlations in both phase and amplitude introduced by the
Gabor wavelets underlying the IrisCode. A simple two-state
Hidden Markov Model is shown to emulate exactly the statistics
of bit sequences generated both from natural and white noise
iris images, including their “imposter” distributions, and may be
useful for generating large synthetic IrisCode databases.
Identifiers
External DOI: https://doi.org/10.1109/TIFS.2015.2500196
This record's URL: https://www.repository.cam.ac.uk/handle/1810/252470
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