Iris Recognition

1.    Report Title
2.    Certificate
3.    Contents
4.    Introduction
5.    Literature survey
6.    Problem statement with proposed solution
a.    History of biometric recognition
b.    What is Iris?
c.    Structure of Iris
d.    Iris Recognition
e.    Iris Recognition for Biometrics
f.     Advantages
g.    Shortcomings
h.    Implementation of Iris Recognition
i.      Issues in Iris Recognition
j.      The Daugman System
7.    Conclusion
8.    Reference

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Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of the irides of an individual's eyes, whose complex random patterns are unique and can be seen from some distance.
Not to be confused with another, less prevalent, ocular-based technology, retina scanning, iris recognition uses camera technology with subtle infrared illumination to acquire images of the detail-rich, intricate structures of the iris. Digital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or someone pretending to be that individual. Databases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates per second per (single-core) CPU, and with infinitesimally small False Match rates.
Almost all publicly operational iris recognition systems worldwide today deploy, as licensed executables, the algorithms described on this website. Some well-known licensees and their brands include LG, Oki, Panasonic, Sagem, IrisGuard, Sarnoff, IRIS, Privium, CHILD Project, CanPass, and Clear (RT). Worldwide some 60 million persons, of some 170 nationalities, have been enrolled by these algorithms for automatic recognition by iris patterns. I hope you find both this overview and the more detailed scientific and mathematical pages on this website interesting.
The technology combines computer vision, pattern recognition, statistical inference, and optics. Its purpose is real-time, high confidence recognition of a person's identity by mathematical analysis of the random patterns that are visible within the iris of an eye from some distance. Because the iris is a protected internal organ whose random texture is complex, unique, and very stable throughout life, it can serve as a kind of living passport or password that one need not remember but can always present. Because the randomness of iris patterns has very high dimensionality, recognition decisions are made with confidence levels high enough to support rapid and reliable exhaustive searches through national-sized databases. The algorithms for iris recognition were developed at Cambridge University by John Daugman.
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The major applications of this technology so far have been: substituting for passports in automated border crossing; expediting security screening at airports; controlling access to restricted areas; Children's Identification and Location Databases (CHILD); school and hospital settings including mother-infant pairing in maternity wards; database access and login; prisoner booking and release; detainee identification and suspect tracking in Iraq and Afghanistan; and "watch list" screening at border crossings. In several countries it is being considered for biometric Identity Cards. Airports worldwide use these algorithms with iris cameras for passenger screening and immigration control in lieu of passport presentation, including 10 UK airport terminals, Amsterdam Schiphol, Frankfurt, 8 Canadian airports, and many US airports in the Registered Traveller "Clear" programme. In UK project IRIS (Iris Recognition Immigration System), over a million frequent travellers have registered in this programme for automated border-crossing using iris recognition. Such passengers do not even need to assert their identity; they just look at the camera in the automated lanes. On the Pakistan Afghanistan border, the United Nations High Commission for Refugees has used these algorithms for anonymous identification of returning Afghan refugees receiving cash grants at voluntary repatriation centres. The earliest large-scale deployment of these algorithms has been operational since 2001 in the United Arab Emirates, where every day about 12 Billion iris comparisons are performed in real-time database searches. Travellers entering the UAE at its 35 air, land, and sea ports have their IrisCodes quickly computed and compared against all the IrisCodes in a central database; you can find out more about that application here. An even larger deployment is for ID cards in the Indian state of Andhra Pradesh, which you can read about here. Iris recognition is forecast to play a role in a wide range of other applications in which a person's identity must be established or confirmed more reliably than merely by possession of documents or codes.
The Daugman algorithms for iris recognition are owned today by L1 Identity Solutions and licensed through its subsidiary Securimetrics. The algorithms won the British Computer Society's IT Award and Medal; the Smithsonian Award; and the "Time 100" Innovation Award. The technology was designated a Millennium Product by the UK Design Council, and Daugman was one of three Finalists for the European Inventor of the Year Award 2009.
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This paper examines automated iris recognition as a biometrically based technology for personal identification and verification. The motivation for this endeavor stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment. In particular, the biomedical literature suggests that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine vision system. The body of this paper details issues in the design and operation of such systems. For the sake of illustration, extant systems are described in some amount of detail.
In this paper, the author has explained the need for iris recognition, and he has also explained why iris can be considered as a medium for biometric security. He has also highlighted the issues which can be faced in every phase of implementing iris recognition systems. The author has also compared two iris recognition systems, and also given his opinion and conclusion of this observations of iris recognition as a biometric tool.
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1) What is Biometrics?
Biometrics can be called as the parts of the human body which give an unique identity to an individual. Biometric, or biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. Examples of biometric identifiers are Fingerprints, Hand-Writing, Voice, Face, Retina, Palm lines etc.

2) What is Biometric Recognition?
Biometrics Recognition refers to the identification of humans by their characteristics, mostly their physical characteristics. Biometrics is used in computer science as a form of identification. Biometrics can be used for enforcing security, or verify authenticity of an individual, or for authorization purposes.

3) Issues with Current Biometric Recognition Technologies
Current biometric recognition technologies are highly invasive, i.e. they do not offer privacy. Typically, the operator is required to make physical contact with a sensing device or otherwise take some special action (e.g., recite a specific sequence).

4) Face Recognition

One possible alternative to the current biometric recognition technologies which has the potential to be less invasive is automated face recognition. However, automated face recognition is still a topic of active research.

Automated iris recognition is yet another alternative for non-invasive verification and identification of people. Interestingly, the spatial patterns that are present in the human iris are highly distinctive to an individual. Unlike the human face, however, the difference in appearance of any one iris might be enough unique to make possible an automated recognition system based on currently available machine vision technologies.
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1) The word iris is derived from the Greek goddess of the rainbow, due to the many colours of the iris.

2) Definition: “The iris (plural: irides or irises) is a thin, circular structure in the eye, responsible for controlling the diameter and size of the pupils and thus the amount of light reaching the retina.”

3) "Eye color" is the color of the iris, which in humans can be green, blue, or browneffing wormsit can be hazel (a combination of light brown, green and gold), grey, violet, or even pink.

4) In response to the amount of light entering the eye, muscles attached to the iris expand or contract the aperture at the center of the iris, known as the pupil. The larger the pupil, the more light can enter.


The iris consists of two layers: the front layer known as a stroma and, back layer which is the pigmented epithelial cells.
The stroma connects to a sphincter muscle (sphincter pupillae), which contracts the pupil in a circular motion, and a set of dilator muscles (dilator pupillae) which pull the iris radially to enlarge the pupil, pulling it in folds.
The back surface is covered by a heavily pigmented epithelial layer that is two cells thick. The high pigment content blocks light from passing through the iris to the retina, restricting it to the pupil.
The iris is divided into two major regions:
1. The pupillary zone is the inner region whose edge forms the boundary of the pupil.
2. The ciliary zone is the rest of the iris that extends to its origin at the ciliary body.
The collarette is the thickest region of the iris, separating the pupillary portion from the ciliary portion.
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“Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of the irises of an individual's eyes, whose complex random patterns are unique and can be seen from some distance.”


Iris recognition for an individual is carried out on the basis of the structure of the iris. This is because the structure of the iris offers the following characteristics:
• The structure of the iris is unique to an individual. During the course of examining large numbers of eyes, researchers have noted that the detailed pattern of an iris, even the left and right iris of a single person, seems to be highly distinctive.

• The structure of the iris is of an individual is stable with age. In research cases with repeated observations, the iris patterns seemed to vary little, at least past childhood.

• While the general structure of the iris is genetically determined, the particulars of its minutiae are critically dependent on circumstances. Therefore, they are highly unlikely to be replicated, in natural means.

• Another interesting aspect of the iris from a biometric point of view has to do with its moment-to-moment dynamics. Due to the complex interplay of the iris’ muscles, the diameter of the pupil is in a constant state of small oscillation. Potentially, this movement could be monitored to make sure that a live specimen is being evaluated.

• Further, since the iris reacts very quickly to changes in impinging illumination (e.g., on the order of hundreds of milliseconds for contraction), monitoring the reaction to a controlled illuminant could provide similar evidence.
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The iris of the eye has been described as the ideal part of the human body for biometric identification for several reasons:
• It is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane (the cornea). This distinguishes it from fingerprints, which can be difficult to recognize after years of certain types of manual labor.
• The iris is mostly flat, and its geometric configuration is only controlled by two complementary muscles (the sphincter pupillae and dilator pupillae) that control the diameter of the pupil. This makes the iris shape far more predictable than, for instance, that of the face.
• The iris has a fine texture that—like fingerprints—is determined randomly during embryonic gestation. Like the fingerprint, it is very hard (if not impossible) to prove that the iris is unique. However, there are so many factors that go into the formation of these textures (the iris and fingerprint) that the chance of false matches for either is extremely low. Even genetically identical individuals have completely independent iris textures.
• An iris scan is similar to taking a photograph and can be performed from about 10 cm to a few meters away. There is no need for the person being identified to touch any equipment that has recently been touched by a stranger, thereby eliminating an objection that has been raised in some cultures against fingerprint scanners, where a finger has to touch a surface, or retinal scanning, where the eye must be brought very close to an eyepiece (like looking into a microscope).
• The commercially deployed iris-recognition algorithm, John Daugman's IrisCode, has an unprecedented false match rate (better than 10−11 if a Hamming distance threshold of 0.26 is used, meaning that up to 26% of the bits in two IrisCodes are allowed to disagree due to imaging noise, reflections, etc., while still declaring them to be a match).[5]
• Many commercial iris scanners can be easily fooled by a high quality image of an iris or face in place of the real thing.

• The accuracy of scanners can be affected by changes in lighting.

• Iris scanners are significantly more expensive than some other forms of biometrics, password or proxy card security systems.

• Iris scanning is a relatively new technology and is incompatible with the very substantial investment that the law enforcement and immigration authorities of some countries have already made into fingerprint recognition.

• Iris recognition is very difficult to perform at a distance larger than a few meters and if the person to be identified is not cooperating by holding the head still and looking into the camera. However, several academic institutions and biometric vendors are developing products that claim to be able to identify subjects at distances of up to 10 meters ("standoff iris" or "iris at a distance" as well as "iris on the move" for persons walking at speeds up to 1 meter/sec).

• As with other photographic biometric technologies, iris recognition is susceptible to poor image quality, with associated failure to enroll rates.

• As with other identification infrastructure (national residents databases, ID cards, etc.), civil rights activists have voiced concerns that iris-recognition technology might help governments to track individuals beyond their will.

• Researchers have tricked iris scanners using images generated from digital codes of stored irises. Criminals could exploit this flaw to steal the identities of others.

• Alcohol consumption causes recognition degradation as the pupil dilates/constricts causing deformation in the iris pattern.
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The first use of iris recognition as a basis for personal identification goes back to efforts to distinguish inmates in the Parisian penal system by visually inspecting their irises, especially the patterning of color.
More recently, the concept of automated iris recognition was proposed by Flom and Safir. It does not appear, however, that this team ever developed and tested a working system.
Early work toward actually realizing a system for automated iris recognition was carried out at Los Alamos National Laboratories, CA.
Subsequently, two research groups developed and documented prototype iris recognition systems.
In 1987 two Ophthalmology Professors, Leonard Flom, M.D.(NYU) and Aran Safir,M.D.(U.Conn), were issued a first of its kind, broad patent # 4,641,349 entitled "Iris Recognition Technology." Subsequently, John Daugman,PhD (Harvard Computer Science faculty) was then salaried by both ophthalmologists to write the algorithm for their concept based upon an extensive series of high resolution iris photos supplied to him by Dr.Flom from his volunteer private patients. Several years later, Daugman received a method patent for the algorithm and a crudely constructed prototype proved the concept. The three individuals then founded "IridianTechnologies,Inc." and assigned the Flom/Safir patent to that entity that was then capitalized by GE Capital, a branch of "GE"(General Electric) and other investors.
"Iridian" then licensed several corporations to the exclusive Daugman algorithm under the protection of the Flom/Safir broad umbrella patent listed above; thus, preventing other algorithms from competing. Upon expiration of the Flom/Safir patent in 2008 other algorithms were patented and several were found to be superior to Daugman's and are now being funded by U.S. Government agencies.

Conceptually, issues in the design and implementation of a system for automated iris recognition can be subdivided into three parts:

A. The first set of issues surrounds image acquisition.

B. The second set is concerned with localizing the iris per se from a captured image.

C. The third part is concerned with matching an extracted iris pattern with candidate data base entries.
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One of the major challenges of automated iris recognition is to capture a high-quality image of the iris while remaining noninvasive to the human operator. Given that the iris is a relatively small (typically about 1 cm in diameter), dark object and that the human operators are very sensitive about their eyes, this matter requires careful engineering. Several points are of particular concern.
First, it is desirable to acquire images of the iris with sufficient resolution and sharpness to support recognition.
Second, it is important to have good contrast in the interior iris pattern without resorting to a level of illumination that annoys the operator.
Third, these images must be well framed (i.e., centered) without unduly constraining the operator (i.e., preferably without requiring the operator to employ an eye piece, chin rest, or other contact positioning that would be invasive).
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Without placing undue constraints on the human operator, image acquisition of the iris cannot be expected to yield an image containing only the iris. Rather, image acquisition will capture the iris as part of a larger image that also contains data derived from the immediately surrounding eye region.

Therefore, prior to performing iris pattern matching, it is important to localize that portion of the acquired image that corresponds to an iris.

If the eyelids are occluding part of the iris, then only that portion of the image below the upper eyelid and above the lower eyelid should be included.

The localizing procedure is performed with the Hough transforms , which uses the parameters as the iris boundary contours.

The Hough transform is a feature extraction technique used in image analysis, computer vision, and digital image processing.


Having localized the region of an acquired image that corresponds to the iris, the final task is to decide if this pattern matches a previously stored iris pattern.

This matter of pattern matching can be decomposed into four parts:
i. Alignment
ii. Representation
iii. Goodness of match
iv. Decision

i) Alignment

Alignment is bringing the newly acquired iris pattern into spatial alignment with a candidate data base entry.

It is used to make a detailed comparison between two images; it is advantageous to establish a precise correspondence between characteristic structures across the pair.

ii) Representation

It involves choosing a representation of the aligned iris patterns that makes their distinctive patterns apparent.

The distinctive spatial characteristics of the human iris are manifest at a variety of scales.

For example, distinguishing structures range from the overall shape of the iris to the distribution of tiny crypts and detailed texture.

To capture this range of spatial detail, it is advantageous to make use of a multiscale representation.

iii) Goodness of Match

It is evaluating the goodness of match between the newly acquired and data base representations.

Given the systems’ controlled image acquisitions and abilities to bring data base entry and newly acquired data into precise alignment, an appropriate match metric can be based on direct point-wise comparisons between primitives in the corresponding representations.

iv) Decision

It is used for deciding if the newly acquired data and the data base entry were derived from the same iris based on the goodness of match.

The final task that must be performed for current purposes is to evaluate the goodness-of-match values into a final judgment as to whether the acquired data does (authentic) or does not (imposter) come from the same iris as does the data base entry. This is done by choosing a separation point in the space of (normalized) Hamming distances between iris representations. Distances smaller than the separation point will be taken as indicative of authentics; those larger will be taken as indicative of imposters.
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The Daugman System is developed by John Daugman.

The Daugman System’s Iris Recognition Algorithm was commercialized in the 1990s.

The components present in the Daugman System are Image-acquisition, Iris-localization, and Pattern-matching.

The Daugman System is capable of three functional modes of operation:
– Enrollment
– Verification
– Identification


For at least a century, it has been suggested that the iris can subserve biometrically based recognition of human individuals.

Recent efforts in machine vision have yielded automated systems that take strides toward realizing this potential. As currently instantiated, these systems are relatively compact and efficient and have shown promising performance in preliminary testing.

An important direction for future efforts is the design and execution of controlled, large-scale research studies. Only via reference to such studies can the true accuracy of iris recognition be determined for both the verification and identification tasks.

Another potential direction for future research would be to relax the constraints under which current iris-recognition systems operate. With this in mind, it would be particularly desirable to decrease the required level of operator participation even while increasing the physical distance from which evaluation takes place.

If such goals can be achieved, then iris recognition can provide the basis for truly noninvasive biometric assessment. Further, if these enhancements can be had while maintaining compact, efficient, and low-cost implementations, then iris recognition will be well positioned for widespread deployment.


The paper referenced is “Iris Recognition: An Emerging Biometric Technology”, which is authored by Richard P. Wildes.

Richard P. Wildes is also a member IEEE. He is also the co-creator of the Wildes Iris Recognition technology.
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