Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns

  • John Daugman
  • Published 2001 in International Journal of Computer Vision


Algorithms first described in 1993 for recognizing persons by their iris patterns have now been tested in several public field trials, producing no false matches in several million comparison tests. The underlying recognition principle is the failure of a test of statistical independence on texture phase structure as encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 244 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm2 over the iris, enabling real-time decisions about personal identity with extremely high confidence. This paper reviews the current algorithms and presents the results of 2.3 million comparisons among eye images acquired in trials in Britain, the USA, and Japan, and it discusses aspects of the process still in need of improvement.

DOI: 10.1023/A:1012365806338

12 Figures and Tables

Citations per Year

323 Citations

Semantic Scholar estimates that this publication has 323 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Daugman2001StatisticalRO, title={Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns}, author={John Daugman}, journal={International Journal of Computer Vision}, year={2001}, volume={45}, pages={25-38} }