John Daugman
Adv. Artif. Intell. Mach. Learn., 4 (2):2152-216
John Daugman : Department of Computer Science and Technology University of Cambridge
DOI: https://dx.doi.org/10.54364/AAIML.2024.42123
Article History: Received on: 03-Feb-24, Accepted on: 29-Mar-24, Published on: 04-Apr-24
Corresponding Author: John Daugman
Email: John.Daugman@CL.cam.ac.uk
Citation: John Daugman (2024). Understanding Biometric Entropy and Iris Capacity: Avoiding Identity Collisions on National Scales. Adv. Artif. Intell. Mach. Learn., 4 (2 ):2152-216
The numbers of persons who can be enrolled by
their iris patterns with no identity collisions is studied in relation
to the biometric entropy extracted, and the decision operating
threshold. The population size at which identity collision becomes
likelier than not, given those variables, defines iris “capacity.”
The general solution to this combinatorial problem is derived, in
analogy with the well-known “birthday problem.” Its application
to unique biometric identification on national population scales
is shown, referencing empirical data from US NIST (National
Institute of Standards and Technology) trials involving 1.2 trillion
(1.2 × 1012) iris comparisons. The entropy of a given person’s
two iris patterns suffices for global identity uniqueness