Understanding Biometric Entropy and Iris Capacity: Avoiding Identity Collisions on National Scales

TL;DR

This paper analyzes the maximum number of individuals uniquely identifiable by iris patterns without collisions, using biometric entropy and thresholds, and applies the findings to large-scale national identification systems.

Contribution

It derives a general solution to the iris capacity problem, analogous to the birthday problem, and applies it to empirical NIST iris comparison data for large populations.

Findings

Iris entropy determines global identity uniqueness.

Capacity estimates for large-scale biometric identification.

Empirical data supports theoretical capacity calculations.

Abstract

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 x 10^(12) ) iris comparisons. The entropy of a given person's two iris patterns suffices for global identity uniqueness.