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. 
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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.

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