Iris recognition identifies a person from the intricate texture of the colored ring around the pupil. The iris’s pattern is highly detailed, stable over a lifetime, and different even between identical twins and between a person’s own left and right eyes, which makes it one of the most discriminating biometrics known.
The dominant method was developed by John Daugman at the University of Cambridge, who patented his core algorithm in 1994. His approach uses mathematical filters called 2D Gabor wavelets to analyze the iris texture and encode it into a compact binary string, the “IrisCode.” Two IrisCodes are then compared by a simple bitwise measure - the fraction of bits that differ, known as the Hamming distance. Because each iris contributes so many effectively independent bits of information, the chance of two different people producing similar codes is extraordinarily small, which is what allows iris systems to search huge databases with very low false-match rates.
That property is why iris recognition scales to national systems. Daugman’s algorithms underpin India’s Aadhaar program, where over a billion residents are enrolled by iris and fingerprint, and the United Arab Emirates’ border-crossing system, where the relevant database has accumulated hundreds of billions of iris cross-comparisons. The same technique was chosen by the Worldcoin project to verify unique human identities.
Why business readers should care: iris recognition shows how a well-chosen biometric plus a compact, fast-to-compare encoding can support population-scale identity at low error rates. It also concentrates the trade-off at the heart of all biometrics - the same uniqueness that makes the iris a near-perfect key makes the irreversible loss of that data, if a stored template is compromised, a permanent problem, since you cannot reissue your eyes.