Collision Avoidance on National and Global Scales: Understanding and Using Big Biometric Entropy

@inproceedings{Daugman2021CollisionAO,
  title={Collision Avoidance on National and Global Scales: Understanding and Using Big Biometric Entropy},
  author={John G. Daugman},
  year={2021}
}
  • J. Daugman
  • Published 24 February 2021
  • Computer Science
Large-scale testing involving more than a trillion comparisons between the iris patterns of different persons has confirmed that the entropy of IrisCodes is large enough to deliver collision avoidance (absence of False Matches) in population sizes of national, continental, and even planetary scale. This short paper explains how the entropy of this biometric achieves it. 

Figures and Tables from this paper

Information Theoretical Analysis of the Uniqueness of Iris Biometrics
Information Theoretical Analysis Of the Uniqueness of Iris Biometrics
Complex-valued Iris Recognition Network
TLDR
This work designs a fully complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture and exploits visualization schemes to convey how thecomplex-valued network, when in comparison to standard real-valued networks, extract fundamentally different features from the iri texture.

References

SHOWING 1-10 OF 11 REFERENCES
The importance of being random: statistical principles of iris recognition
Radial correlations in iris patterns, and mutual information within IrisCodes
TLDR
The authors measured the mutual information entailed by radial correlations within each of 632,500 different iris patterns from persons of 152 nationalities and show that a non-uniform allocation of encoding resolution radially leads to significant performance improvements by reducing redundancy.
IREX III: Performance of Iris Identification Algorithms
Disclaimer Specific hardware and software products identified in this report were used in order to perform the evaluations described in this document. In no case does identification of any commercial
Information Theory and the IrisCode
  • J. Daugman
  • Computer Science
    IEEE Transactions on Information Forensics and Security
  • 2016
TLDR
A simple two-state hidden Markov model is shown to emulate exactly the statistics of bit sequences generated both from natural and white noise iris images, including their imposter distributions, and may be useful for generating large synthetic IrisCode databases.
Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
TLDR
In a comprehensive comparison of face identification by humans and computers, it is found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test.
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
TLDR
The MegaFace dataset is assembled, both for identification and verification performance, and performance with respect to pose and a persons age is evaluated, as a function of training data size (#photos and #people).
AADHAAR: A Biometric History of India’s 12-Digit Revolution
  • New Delhi: Westland Publications,
  • 2017
Ongoing Face Recognition Vendor Test (FRVT) Part 2: Identification
Elements of information theory (2. ed.)
...
...