Recognizing people by their iris patterns

Abstract

Performance of face recognition schemes is bounded at one limit by a genotypic error rate (the birth rate of identical twins), and at another limit by a phenotypic error rate (change in facial appearance over time). These set minimal False Accept and False Reject frequencies by undermining the between-class variability in the first case and increasing the withinclass variability in the second. It would be preferable to base recognition decisions upon features which had very little genetic penetrance, yet high complexity, and stability over the lifetime of the individual. Phenotypic facial features do exist with exactly these properties. When imaged at a distance of up to about one metre, the population entropy (information density) of iris patterns is roughly 3.4 bits per square millimetre, and their complexity spans about 266 independent degrees-of-freedom. These statistics exceed significantly the degrees of randomness and complexity available in other identifying biometric patterns.