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

arXiv: https://arxiv.org/abs/2308.03189 · HTML: https://mlsanity.com/paper/2308.03189/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [On the Capacity of Face Representation](https://mlsanity.com/paper/1709.10433/) (2017)

## Built on by (most cited first, 1 of 1)

- [Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems](https://mlsanity.com/paper/2508.11419/) (2025)

## Abstract (arXiv)

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.