John Daugman OBE FREng
Professor of Computer Vision and Pattern Recognition

short biographical sketch

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Lecture courses:

• Information Theory
• Mathematical Methods in Computer Science
• Computer Vision
  • video lecture recordings: Biological and Machine Vision

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Research interests:

Computer vision, pattern recognition, neural computing, information theory.

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Selected papers:

• Daugman J (2024) "Understanding Biometric Entropy and Iris Capacity: Avoiding Identity Collisions on National Scales." Advances in Artificial Intelligence and Machine Learning, 4(2), pp 2152-2163.

• Daugman J, Downing C, Oluwatobi Akande, Oluwakemi Abikoye (2023) "Ethnicity and Biometric Uniqueness: Iris Pattern Individuality in a West African Database." IEEE Transactions on Biometrics, Behavior, and Identity Science, 6(1), pp 79-86.

• Daugman J (2022) Video lecture, 28 minutes (70 MB .mp4 video file): "Biometric Entropy, Information Capacity, and Iris Recognition". Also available here in the cloud.

• Daugman J and Downing C (2020) "Broken Symmetries, Random Morphogenesis, and Biometric Distance." IEEE Transactions on Biometrics, Behavior, and Identity Science (July 2020) 2(3), pp 271-278. ISSN: 2637-6407 (8 May 2020). DOI: 10.1109/TBIOM.2020.2993225

• Daugman J and Downing C (2019) "Radial correlations in iris patterns, and mutual information within IrisCodes." IET Biometrics (May 2019) 8(3), pp 185-189. Online at IET Digital Library, or download PDF

• Daugman J (2017) "Biometric entropy" (summary of IAPR Award Lecture). IAPR Newsletter (July 2017), 39(3), pp 4-5. PDF

• Daugman J and Downing C (2016) "Searching for doppelgängers: assessing the universality of the IrisCode impostors distribution." IET Biometrics (Jan 2016) 5(2), pp 65-75. Online at IET Digital Library, or download PDF

• Daugman J and Downing C (2017) "Iris image quality metrics with veto power and nonlinear importance tailoring." Chapter 4 in: Iris and Periocular Biometric Recognition (C Rathgeb and C Busch, eds), IET Publishing, ISBN: 978-1-78561-168-1, pp 83-100. PDF

• Daugman J (2015) "Information Theory and the IrisCode." IEEE Trans. Info.Foren.Sec 11(2), pp 400-409. (PDF)

• Hao F, Daugman J, Zielinski P (2008) "A fast search algorithm for a large fuzzy database." IEEE Trans. Information Forensics and Security 3(2), pp 203-212. ( PDF)

• Daugman J (2007) "New methods in iris recognition." IEEE Trans. Systems, Man, Cybernetics B 37(5), pp 1167-1175. ( PDF)

• Daugman J (2006) "Probing the uniqueness and randomness of IrisCodes: Results from 200 billion iris pair comparisons." Proceedings of the IEEE, 94(11), pp 1927-1935. ( PDF)

• Daugman J (2003) "Demodulation by complex-valued wavelets for stochastic pattern recognition." Int'l Journal of Wavelets, Multi-resolution and Information Processing, 1(1), pp 1-17. ( PDF)

• Daugman J (2003) "The importance of being random: Statistical principles of iris recognition." Pattern Recognition, 36(2), pp 279-291. ( PDF)

• Daugman J (2002) "Gabor wavelets and statistical pattern recognition." The Handbook of Brain Theory and Neural Networks, 2nd ed., MIT Press (M. Arbib, editor), pp 457-463.

• Daugman J (2001) "Statistical richness of visual phase information." Int'l Journal of Computer Vision, 45(1), pp 25-38.

• Daugman J and Downing C (2001) "Epigenetic randomness, complexity, and singularity of human iris patterns." Proceedings of the Royal Society, B, 268, Biological Sciences, pp 1737 - 1740. (PDF)

• Daugman J (2001) "Brain metaphor and brain theory." Chapter 2 in Philosophy and the Neurosciences, edited by W. Bechtel et al. Oxford: Blackwell Publishers. (Scanned PDF here)

• Daugman J (2000) "Biometric decision landscapes." Technical Report No. TR482, University of Cambridge Computer Laboratory. (PDF)

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Iris Recognition

All other links on this page relate to IRIS RECOGNITION, a practical application of the work in computer vision, wavelets, and statistical pattern recognition.

• A billion persons enrolled: International deployments of these iris recognition algorithms.

• Flagship deployment in India (Update: now 1.25 Billion citizens enrolled) (short article, PDF).

• History of iris recognition.

• General introduction (purpose, principle, current applications).

• "How Iris Recognition Works" (.pdf reprint: J. Daugman (2004), IEEE Trans. CSVT 14(1), pp. 21 - 30.)

• An iris with its "IrisCode" (and localization graphics). More examples of IrisCodes.

• Detailed colour iris image, and another.

• What iris patterns reveal in infrared light.

• Some pictures and examples of deployments of the Daugman algorithms.

• Quick Primer on Hamming Distances for Iris Recognition (4-page PDF)

• Summary of statistical results from 200 billion iris cross-comparisons (spanning 152 nationalities in the UAE border-crossing database). More detailed report with full Probability Tables available here.

• Examples of deployments at airports and border-crossings (.pdf article from Encyclopedia of Biometrics, 2010).

• "Effect of Severe Image Compression on Iris Recognition Performance" (.pdf reprint: J. Daugman and C. Downing (2008), IEEE Trans. Inform. Foren. & Secur. 3(1), pp. 52 - 61.)

• A large "watch-list" national security deployment of these algorithms.

• Interview in BioSocieties (2008) about biometrics, anonymity, privacy, and the Liberal State.

• Iris matching engine, and search speed.

• Decision environment (separability of same vs different iris patterns).

• Performance of these algorithms compared to other biometrics (UK National Physical Laboratory test report, 2001).

• Mathematical explanation of "IrisCodes" and iris recognition.

• Independence of bits across IrisCodes.

• Binomial Distribution of unrelated IrisCodes (histogram of 9.1 million raw Hamming Distances).

• 9.1 million comparisons between unrelated IrisCodes (with rotations for best match).

• Quantile-Quantile comparison between the data and binomial theory (9.1 million comparisons).

• Why false match probability does not accumulate in large database searches.

• What about genetically identical iris patterns (e.g. identical twins)?

• Anatomy, physiology, and development of the iris.

• Operators for localizing the iris within an image.

• Advantages and disadvantages of the iris for identification.

• Statistical demands of identification vs verification.

• Is there advantage in combining multiple biometrics?

• Links to some licensees and users of these algorithms; and partial list of applications.

• Iris cameras that use these algorithms.

• How these algorithms identified the National Geographic Afghan girl, 18 years later.

• What about "iridology?"

• More humour: "Iris recognition and The Simpsons Movie"

• Cartoons about iris recognition from the British press.

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