How Iris Recognition Works

TLDR

Biometric systems lacking iris-based discrimination can only support one‑to‑one verification or limited comparisons. The paper presents iris recognition algorithms and reports results from 9.1 million comparisons across trials in Britain, the USA, Japan, and Korea. Iris recognition relies on detecting statistical dependence in iris phase structure encoded by multi‑scale quadrature wavelets. The algorithms achieved zero false matches in several million tests, with a 249‑degree‑of‑freedom phase space yielding ~3.2 b/mm² discrimination entropy that enables real‑time, high‑confidence identification in exhaustive database searches.

Abstract

Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.