In a 1953 clinical textbook, Physiology of the Eye, F.H. Adler wrote (Chapter VI, page 143): "In fact, the markings of the iris are so distinctive that it has been proposed to use photographs as a means of identification, instead of fingerprints." Apparently Adler referred to comments by the British ophthalmologist J.H. Doggart, who in 1949 had written (OSSLM, page 27) that: "Just as every human being has different fingerprints, so does the minute architecture of the iris exhibit variations in every subject examined. [Its features] represent a series of variable factors whose conceivable permutations and combinations are almost infinite." Much later in the 1980's two American ophthalmologists, L. Flom and A. Safir managed to patent Adler's and Doggart's conjecture that the iris could serve as a human identifier, but they had no actual algorithm or implementation to perform it and so the patent was conjecture. The roots of the conjecture stretch back even further: in 1892 the Frenchman A. Bertillon had documented nuances in "Tableau de l'iris humain". Divination of all sorts of things based on iris patterns goes back to ancient Egypt, to Chaldea in Babylonia, and to ancient Greece, as documented in stone inscriptions, painted ceramic artefacts, and the writings of Hippocrates. Iris divination persists today, as "iridology."

The core theoretical idea in Daugman's algorithms is that the failure of a test of statistical independence could be a very strong basis for pattern recognition, if there is sufficiently high entropy (enough degrees-of-freedom of random variation) among samples from different classes. In 1994 he patented this basis for iris recognition and its underlying Computer Vision algorithms for image processing, feature extraction, and matching, and published the paper "High confidence visual recognition of persons by a test of statistical independence" in IEEE Transactions on Pattern Analysis and Machine Intelligence. Those original algorithms became widely licensed through a series of companies (IriScan, Iridian, Sarnoff, Sensar, LG-Iris, Panasonic, Oki, BI2, IrisGuard, Unisys, Sagem, Enschede, Securimetrics and L1, now owned by the French company Safran/Morpho). With various improvements over the years, these algorithms remain today the basis of all significant public deployments of iris recognition. But academic research on many aspects of this technology has recently exploded. As noted in a survey by Bowyer et al., during the last few years there have been more than 1,000 papers published that address optics, photonics, sensors, biology, genetics, ergonomics, interfaces, decision theory, coding, compression, protocol, security, hardware and algorithmic aspects of this technology.

Optical systems for iris image acquisition have shown perhaps the most impressive advances, enabling generally a more flexible user interface and a more comfortable distance between camera and subject than the "in-your-face" experience and the "stop-and-stare" interface of the first cameras. Pioneering work by Dr. J. Matey and his team at Sarnoff Labs led to the current generation of systems capturing "iris-at-a-distance" and "iris-on-the-move," in which capture volume is nearly a cubic meter and on-the-move means walking at 1 meter/second, enabling throughput rates of a person per second. There has been a kind of long-distance race to demonstrate the longest stand-off distance, with some claims extending to the tens of meters. The camera is then essentially a telescope, but the need to project enough radiant light safely onto the target to overcome its inverse square-law dilution is a limitation. These developments bring two wry thoughts to my mind: First, I recall that when I originally began giving live demonstrations of iris recognition, the capture volume was perhaps a cubic inch; the hardware was a wooden box containing a videocamera, a video display, a near-infrared light source, and a voice interface that replayed the name of a person when visually identified. Second, I have read that the Hubble Space Telescope is to be decommissioned, and I wonder whether it might be converted into the Hubble Iris Camera for the ultimate "iris-at-a-distance" demonstration...

Most flagship deployments of the Daugman algorithms for iris recognition have been at airports, in lieu of passport presentation and for security screening using watch-lists. In the years soon after 2000, major deployments began at Amsterdam's Schiphol Airport and at 10 UK airport terminals allowing frequent travellers to present their iris instead of their passport, in a programme called IRIS: Iris Recognition Immigration System. Similar systems exist along the US / Canadian border, and many others. In the United Arab Emirates, all 32 air, land, and sea-ports deploy these algorithms to screen all persons entering the UAE requiring a visa. Because a large watch-list compiled among GCC States is exhaustively searched each time, the number of iris cross-comparison has climbed to 62 trillion in 10 years. But by far the most breathtaking deployment began operation in 2011 in India, whose Government is enrolling the iris patterns (and other biometrics) of all 1.2 billion citizens for the Aadhaar scheme for entitlements distribution, run by the Universal IDentification Authority of India (UIDAI). This vastly ambitious programme enrolls about 1 million persons every day, across 36,000 stations operated by 83 agencies. Its purpose is to issue each citizen a biometrically provable unique entitlement number (Aadhaar) by which benefits may be claimed, and social inclusion enhanced; thus the slogan of UIDAI is: "To give the poor an identity." With more than 600 million persons enrolled so far (as of May 2014), against whom the daily intake of another million must be compared to check for duplicate identities, the daily number of iris cross-comparisons is about 600 trillion (600 million--million, or 6 x 10-to-the-14th-power), and growing. A dashboard plotting the progress of this juggernaut (a Hindi word, appropriately) is maintained by the Indian Government here.