TABLE OF CONTENTS Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field deployments, producing no false matches in millions of iris comparisons. The recognition principle is the failure of a test of statistical independence on iris phase structure, as encoded by multi-scale quadrature Gabor 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 bits/mm 2 over the iris, enabling real-time decisions about personal identity with extremely high confidence. These high confidence levels are important because they allow very large databases on even a national scale 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. This chapter explains the iris recognition algorithms, and presents results of 9.1 million comparisons among eye images from trials in the United Kingdom, the United States, Japan, and Korea.
Reliable automatic recognition of persons has long been an attractive goal. As in all pattern recognition problems, the key issue is the relation between interclass and intraclass variability: Objects can be reliably classified only if the variation among different instances of a given class is less than the variation between different classes. For example in face recognition, difficulties arise from the fact that the face is a changeable social organ displaying a variety of expressions, as well...
