{"history":[],"article":{"id":"3892711b-db44-449b-bf4f-f5767f87a1fd","title":"Iris recognition","slug":"iris-recognition","content":":::summary\n:::\n\n# \n**Iris recognition** is an automated method of [biometric](https://bliptext.com/articles/biometrics) identification that uses mathematical pattern-recognition techniques on video images of one or both of the [irises](https://bliptext.com/articles/iris-anatomy) of an individual's [eyes](https://bliptext.com/articles/human-eye), whose complex patterns are unique, stable, and can be seen from some distance.\nThe discriminating powers of all [biometric](https://bliptext.com/articles/biometrics) technologies depend on the amount of [entropy](https://bliptext.com/articles/entropy-information-theory) they are able to encode and use in matching.\nIris recognition is exceptional in this regard, enabling the avoidance of \"collisions\" ([False Matches](https://bliptext.com/articles/false-positives-and-false-negatives)) even in cross-comparisons across massive populations.\nIts major limitation is that image acquisition from distances greater than a meter or two, or without cooperation, can be very difficult.\nHowever, the technology is in development and iris recognition can be accomplished from even up to 10 meters away or in a live camera feed.\n[Retinal scanning](https://bliptext.com/articles/retinal-scan) is a different, ocular-based biometric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition.\nIris recognition uses video camera technology with subtle [near infrared](https://bliptext.com/articles/near-infrared) illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.\nDigital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or someone pretending to be that individual.\nDatabases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates per second per (single-core) CPU, and with remarkably low false match rates.\nAt least 1.5 billion people around the world (including 1.29 billion citizens of India, in the UIDAI / [Aadhaar](https://bliptext.com/articles/aadhaar) programme as updated on 30 November) have been enrolled in iris recognition systems for national ID, e-government services, benefits distribution, security, and convenience purposes such as passport-free automated border-crossings.\nA key advantage of iris recognition, besides its speed of matching and its extreme resistance to false matches, is the stability of the iris as an internal and protected, yet externally visible organ of the eye.\nIn 2023, Pakistan's National Database & Registration Authority (NADRA) has launched IRIS for citizen registration/ Civic Management during registration at its offices for the National ID Card.\nAfter its initial stage, the eye-recognition verification access will be available for LEAs, banking sectors, etc.\n# [Iris recognition biometric systems apply mathematical pattern-recognition techniques to images of the irises of an individual's eyes. | upload.wikimedia.org/wikipedia/commons/d/d7/ColourIris.png]\n\n# History\nAlthough [John Daugman](https://bliptext.com/articles/john-daugman) developed and in the 1990s patented the first actual algorithms to perform iris recognition, published the first papers about it and gave the first live demonstrations, the concept behind this invention has a much longer history and today it benefits from many other active scientific contributors.\nIn a 1953 clinical textbook, F.H. Adler wrote: \"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.\" Adler referred to comments by the British ophthalmologist J.H. Doggart, who in 1949 had written 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.\" Later in the 1980s, two American ophthalmologists, L. Flom and [Aran Safir](https://bliptext.com/articles/aran-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 their patent remained conjecture.\nThe roots of this conjecture stretch back even further: in 1892 the Frenchman A. Bertillon had documented nuances in \"Tableau de l'iris humain\".\nDivination 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.\n(Iris divination persists today, as \"[iridology](https://bliptext.com/articles/iridology).\")\nThe core theoretical idea in Daugman's algorithms is that the failure of a test of [statistical independence](https://bliptext.com/articles/statistical-independence) can 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.\nIn 1994 he patented this basis for iris recognition and its underlying computer vision algorithms for image processing, feature extraction, and matching, and published them in a paper.\nThese algorithms became widely licensed through a series of companies: IriScan (a start-up founded by Flom, Safir, and Daugman), Iridian, Sarnoff, Sensar, LG-Iris, Panasonic, Oki, BI2, IrisGuard, Unisys, Sagem, Enschede, Securimetrics and L-1, now owned by French company [Morpho](https://bliptext.com/articles/morpho-safran).\nWith various improvements over the years, these algorithms remain today the basis of all significant public deployments of iris recognition, and they are consistently top performers in NIST tests (implementations submitted by L-1, [Morpho](https://bliptext.com/articles/morpho-safran)Trust and [Morpho](https://bliptext.com/articles/morpho-safran), for whom Daugman serves as Chief Scientist for Iris Recognition).\nBut research on many aspects of this technology and on alternative methods has exploded, and today there is a rapidly growing academic literature on optics, photonics, sensors, biology, genetics, ergonomics, interfaces, decision theory, coding, compression, protocol, security, mathematical and hardware aspects of this technology.\nMost flagship deployments of these algorithms have been at airports, in lieu of passport presentation, and for security screening using watch-lists.\nIn the early years of this century, major deployments began at Amsterdam's Schiphol Airport and at ten 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 / Canada border](https://bliptext.com/articles/canada-united-states-border), and many others.\nIn the United Arab Emirates, all 32 air, land, and seaports deploy these algorithms to screen all persons entering the UAE requiring a visa.\nBecause a large watch-list compiled among GCC States is exhaustively searched each time, the number of iris cross-comparisons climbed to 62 trillion in 10 years.\nThe [Government of India](https://bliptext.com/articles/government-of-india) has enrolled the iris codes (as well as fingerprints) of more than 1.2 billion citizens in the [UIDAI](https://bliptext.com/articles/uidai) (Unique Identification Authority of India) programme for national ID and fraud prevention in entitlements distribution.\nIn a different type of application, iris is one of three biometric identification technologies internationally standardised since 2006 by [ICAO](https://bliptext.com/articles/international-civil-aviation-organization) for use in e-passports (the other two are fingerprint and face recognition).\n\n# Visible vs near infrared imaging\nIris [melanin](https://bliptext.com/articles/melanin), also known as chromophore, mainly consists of two distinct heterogeneous macromolecules, called eu[melanin](https://bliptext.com/articles/melanin) (brown–black) and pheo[melanin](https://bliptext.com/articles/melanin) (yellow–reddish), whose absorbance at longer wavelengths in the NIR spectrum is negligible.\nAt shorter wavelengths within the VW spectrum, however, these chromophores are excited and can yield rich patterns.\nHosseini, et al. provide a comparison between these two imaging modalities.\nAn alternative feature extraction method to encode VW iris images was also introduced, which may offer an alternative approach for multi-modal biometric systems.\n\n# Operating principle\nFirst the system has to localize the inner and outer boundaries of the iris (pupil and limbus) in an image of an eye.\nFurther subroutines detect and exclude eyelids, eyelashes, and specular reflections that often occlude parts of the iris.\nThe set of pixels containing only the iris, normalized by a [rubber-sheet model](https://bliptext.com/articles/rubber-sheet-model) to compensate for pupil dilation or constriction, is then analyzed to extract a bit pattern encoding the information needed to compare two iris images.\nIn the case of Daugman's algorithms, a [Gabor wavelet](https://bliptext.com/articles/gabor-filter) transform is used.\nThe result is a set of complex numbers that carry local amplitude and phase information about the iris pattern.\nIn Daugman's algorithms, most amplitude information is discarded, and the 2048 bits representing an iris pattern consist of phase information (complex sign bits of the Gabor wavelet projections).\nDiscarding the amplitude information ensures that the template remains largely unaffected by changes in illumination or camera gain, and contributes to the long-term usability of the biometric template.\nFor identification (one-to-many template matching) or verification (one-to-one template matching), a template created by imaging an iris is compared to stored templates in a database.\nIf the [Hamming distance](https://bliptext.com/articles/hamming-distance) is below the decision threshold, a positive identification has effectively been made because of the statistical extreme improbability that two different persons could agree by chance (\"collide\") in so many bits, given the high [entropy](https://bliptext.com/articles/entropy) of iris templates.\n# [A, now obsolete, IriScan model 2100 iris recognition camera | upload.wikimedia.org/wikipedia/commons/1/1b/IriScan///model///2100///iris///scanner///1.jpg]\n\n# Advantages\nThe iris of the eye has been described as the ideal part of the human body for biometric identification for several reasons:\nIt is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane (the [cornea](https://bliptext.com/articles/cornea)).\nThis distinguishes it from fingerprints, which can be difficult to recognize after years of certain types of manual labor.\nThe 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.\nThis makes the iris shape far more predictable than, for instance, that of the face.\nThe iris has a fine texture that—like fingerprints—is determined randomly during embryonic [gestation](https://bliptext.com/articles/gestation).\nLike the fingerprint, it is very hard (if not impossible) to prove that the iris is unique.\nHowever, 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.\nEven genetically identical individuals (and the left and right eyes of the same individual) have completely independent iris textures.\nAn iris scan is similar to taking a photograph and can be performed from about 10 cm to a few meters away.\nThere 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).\nThe commercially deployed iris-recognition algorithm, [John Daugman](https://bliptext.com/articles/john-daugman)'s IrisCode, has an unprecedented [false match](https://bliptext.com/articles/type-i-error) 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).\nWhile there are some medical and surgical procedures that can affect the colour and overall shape of the iris, the fine texture remains remarkably stable over many decades.\nSome iris identifications have succeeded over a period of about 30 years.\nIris recognition works with clear [contact lenses](https://bliptext.com/articles/contact-lens), [eyeglasses](https://bliptext.com/articles/eyeglasses), and non-mirrored [sunglasses](https://bliptext.com/articles/sunglasses).\nThe early Sensar technology worked by first finding the face, then the eyes, and then took the Iris images.\nThis was all done using infrared lighting.\nIt is possible to identify someone uniquely in a dark room while they were wearing sunglasses.\nMathematically, iris recognition based upon the original Daugman patents or other similar or related patents define the strongest biometric in the world.\nIris recognition will uniquely identify anyone, and easily discerns between identical twins.\nIf a human can verify the process by which the iris images are obtained (at a customs station, entering or even walking by an embassy, as a desktop 2nd factor for authentication, etc.) or through the use of live eye detection (which varies lighting to trigger slight dilation of the pupil and variations across a quick scan which may take several image snapshots) then the integrity of the identification are extremely high.\n\n# Shortcomings\nMany commercial iris scanners can be easily fooled by a high quality image of an iris or face in place of the real thing.\nThe scanners are often tough to adjust and can become bothersome for multiple people of different heights to use in succession.\nThe accuracy of scanners can be affected by changes in lighting.\nIris scanners are significantly more expensive than some other forms of biometrics, as well as password and [proximity card](https://bliptext.com/articles/proximity-card) security systems.\nIris 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.\nHowever, 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 Princeton Identity's \"Iris on the Move\" for persons walking at speeds up to 1 meter/sec).\nAs with other photographic biometric technologies, iris recognition is susceptible to poor image quality, with associated failure to enroll rates.\nAs 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.\nResearchers have tricked iris scanners using images generated from digital codes of stored irises.\nCriminals could exploit this flaw to steal the identities of others.\nThe first study on surgical patients involved modern cataract surgery and showed that it can change iris texture in such a way that iris pattern recognition is no longer feasible or the probability of falsely rejected subjects is increased.\n\n# Security considerations\nAs with most other biometric identification technology, an important consideration is live-tissue verification.\nThe reliability of any biometric identification depends on ensuring that the signal acquired and compared has actually been recorded from a live body part of the person to be identified and is not a manufactured template.\nBesides a person's physical characteristics, which includes the eyes, one's [voice](https://bliptext.com/articles/speaker-recognition) and [handwriting](https://bliptext.com/articles/handwriting-recognition) too, are not protected by the [Fourth Amendment](https://bliptext.com/articles/fourth-amendment-to-the-united-states-constitution) even though they are all constantly exposed.\nMany commercially available iris-recognition systems are easily fooled by presenting a high-quality photograph of a face instead of a real face, which makes such devices unsuitable for unsupervised applications, such as door access-control systems.\nHowever, this is not the case with all iris recognition algorithms.\nThe problem of live-tissue verification is less of a concern in supervised applications (e.g., [immigration control](https://bliptext.com/articles/immigration-control)), where a human operator supervises the process of taking the picture.\nMethods that have been suggested to provide some defence against the use of fake eyes and irises include changing ambient lighting during the identification (switching on a bright lamp), such that the [pupillary reflex](https://bliptext.com/articles/[pupil](https://bliptext.com/articles/pupil)lary-reflex) can be verified and the iris image be recorded at several different [pupil](https://bliptext.com/articles/pupil) diameters; analysing the 2D spatial frequency spectrum of the iris image for the peaks caused by the printer [dither](https://bliptext.com/articles/dither) patterns found on commercially available fake-iris contact lenses; analysing the temporal frequency spectrum of the image for the peaks caused by computer displays.\nOther methods include using [spectral analysis](https://bliptext.com/articles/spectrometer) instead of merely monochromatic cameras to distinguish iris tissue from other material; observing the characteristic natural movement of an eyeball (measuring nystagmus, tracking eye while text is read, etc.); testing for retinal [retroreflection](https://bliptext.com/articles/retroreflection) ([red-eye effect](https://bliptext.com/articles/red-eye-effect)) or for reflections from the eye's four optical surfaces (front and back of both cornea and lens) to verify their presence, position and shape.\nAnother proposed method is to use 3D imaging (e.g., [stereo cameras](https://bliptext.com/articles/stereo-camera)) to verify the position and shape of the iris relative to other eye features.\nA 2004 report by the German [Federal Office for Information Security](https://bliptext.com/articles/federal-office-for-information-security) noted that none of the iris-recognition systems commercially available at the time implemented any live-tissue verification technology.\nLike any pattern-recognition technology, live-tissue verifiers will have their own false-reject probability and will therefore further reduce the overall probability that a legitimate user is accepted by the sensor.\n\n# Privacy considerations\n\n# Deployed applications\n# [IrisGuard Inc. UAE enrolment station | upload.wikimedia.org/wikipedia/en/d/d4/IrisGuard-UAE.JPG]\n# [IrisGuard Inc. First Cash Withdrawal on Iris Enabled ATM | upload.wikimedia.org/wikipedia/commons/f/f2/Iris///Recognition///Enabled///ATM.jpg]\n# [A U.S. Marine Corps Sergeant uses a \"PIER 2.3\" iris scanner to positively identify a member of the Baghdadi city council prior to a meeting with local tribal leaders, sheiks, community leaders and U.S. service members. | upload.wikimedia.org/wikipedia/commons/f/f6/USMC///Sergeant///identifies///Baghdaddi///city///council///member///with///iris///scanner.jpg]\n\n# Iris recognition in television and movies","current_revision":null,"created_by":"kACnuQmH5k9Nr5VNVEpVeg7XpvUmMctD","created_at":"2025-01-25T19:20:59.310Z","updated_at":"2025-01-25T19:20:59.310Z","search_vector":"'iri':1 'recognit':2","search_content":" \n \n\n# \n**Iris recognition** is an automated method of [biometric](https://bliptext.com/articles/biometrics) identification that uses mathematical pattern-recognition techniques on video images of one or both of the [irises](https://bliptext.com/articles/iris-anatomy) of an individual's [eyes](https://bliptext.com/articles/human-eye), whose complex patterns are unique, stable, and can be seen from some distance.\nThe discriminating powers of all [biometric](https://bliptext.com/articles/biometrics) technologies depend on the amount of [entropy](https://bliptext.com/articles/entropy-information-theory) they are able to encode and use in matching.\nIris recognition is exceptional in this regard, enabling the avoidance of \"collisions\" ([False Matches](https://bliptext.com/articles/false-positives-and-false-negatives)) even in cross-comparisons across massive populations.\nIts major limitation is that image acquisition from distances greater than a meter or two, or without cooperation, can be very difficult.\nHowever, the technology is in development and iris recognition can be accomplished from even up to 10 meters away or in a live camera feed.\n[Retinal scanning](https://bliptext.com/articles/retinal-scan) is a different, ocular-based biometric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition.\nIris recognition uses video camera technology with subtle [near infrared](https://bliptext.com/articles/near-infrared) illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.\nDigital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or someone pretending to be that individual.\nDatabases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates per second per (single-core) CPU, and with remarkably low false match rates.\nAt least 1.5 billion people around the world (including 1.29 billion citizens of India, in the UIDAI / [Aadhaar](https://bliptext.com/articles/aadhaar) programme as updated on 30 November) have been enrolled in iris recognition systems for national ID, e-government services, benefits distribution, security, and convenience purposes such as passport-free automated border-crossings.\nA key advantage of iris recognition, besides its speed of matching and its extreme resistance to false matches, is the stability of the iris as an internal and protected, yet externally visible organ of the eye.\nIn 2023, Pakistan's National Database & Registration Authority (NADRA) has launched IRIS for citizen registration/ Civic Management during registration at its offices for the National ID Card.\nAfter its initial stage, the eye-recognition verification access will be available for LEAs, banking sectors, etc.\n# [Iris recognition biometric systems apply mathematical pattern-recognition techniques to images of the irises of an individual's eyes. | upload.wikimedia.org/wikipedia/commons/d/d7/ColourIris.png]\n\n# History\nAlthough [John Daugman](https://bliptext.com/articles/john-daugman) developed and in the 1990s patented the first actual algorithms to perform iris recognition, published the first papers about it and gave the first live demonstrations, the concept behind this invention has a much longer history and today it benefits from many other active scientific contributors.\nIn a 1953 clinical textbook, F.H. Adler wrote: \"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.\" Adler referred to comments by the British ophthalmologist J.H. Doggart, who in 1949 had written 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.\" Later in the 1980s, two American ophthalmologists, L. Flom and [Aran Safir](https://bliptext.com/articles/aran-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 their patent remained conjecture.\nThe roots of this conjecture stretch back even further: in 1892 the Frenchman A. Bertillon had documented nuances in \"Tableau de l'iris humain\" .\nDivination 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.\n(Iris divination persists today, as \"[iridology](https://bliptext.com/articles/iridology).\")\nThe core theoretical idea in Daugman's algorithms is that the failure of a test of [statistical independence](https://bliptext.com/articles/statistical-independence) can 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.\nIn 1994 he patented this basis for iris recognition and its underlying computer vision algorithms for image processing, feature extraction, and matching, and published them in a paper.\nThese algorithms became widely licensed through a series of companies: IriScan (a start-up founded by Flom, Safir, and Daugman), Iridian, Sarnoff, Sensar, LG-Iris, Panasonic, Oki, BI2, IrisGuard, Unisys, Sagem, Enschede, Securimetrics and L-1, now owned by French company [Morpho](https://bliptext.com/articles/morpho-safran).\nWith various improvements over the years, these algorithms remain today the basis of all significant public deployments of iris recognition, and they are consistently top performers in NIST tests (implementations submitted by L-1, [Morpho](https://bliptext.com/articles/morpho-safran)Trust and [Morpho](https://bliptext.com/articles/morpho-safran), for whom Daugman serves as Chief Scientist for Iris Recognition).\nBut research on many aspects of this technology and on alternative methods has exploded, and today there is a rapidly growing academic literature on optics, photonics, sensors, biology, genetics, ergonomics, interfaces, decision theory, coding, compression, protocol, security, mathematical and hardware aspects of this technology.\nMost flagship deployments of these algorithms have been at airports, in lieu of passport presentation, and for security screening using watch-lists.\nIn the early years of this century, major deployments began at Amsterdam's Schiphol Airport and at ten 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 / Canada border](https://bliptext.com/articles/canada-united-states-border), and many others.\nIn the United Arab Emirates, all 32 air, land, and seaports deploy these algorithms to screen all persons entering the UAE requiring a visa.\nBecause a large watch-list compiled among GCC States is exhaustively searched each time, the number of iris cross-comparisons climbed to 62 trillion in 10 years.\nThe [Government of India](https://bliptext.com/articles/government-of-india) has enrolled the iris codes (as well as fingerprints) of more than 1.2 billion citizens in the [UIDAI](https://bliptext.com/articles/uidai) (Unique Identification Authority of India) programme for national ID and fraud prevention in entitlements distribution.\nIn a different type of application, iris is one of three biometric identification technologies internationally standardised since 2006 by [ICAO](https://bliptext.com/articles/international-civil-aviation-organization) for use in e-passports (the other two are fingerprint and face recognition).\n\n# Visible vs near infrared imaging\nIris [melanin](https://bliptext.com/articles/melanin), also known as chromophore, mainly consists of two distinct heterogeneous macromolecules, called eu[melanin](https://bliptext.com/articles/melanin) (brown–black) and pheo[melanin](https://bliptext.com/articles/melanin) (yellow–reddish), whose absorbance at longer wavelengths in the NIR spectrum is negligible.\nAt shorter wavelengths within the VW spectrum, however, these chromophores are excited and can yield rich patterns.\nHosseini, et al. provide a comparison between these two imaging modalities.\nAn alternative feature extraction method to encode VW iris images was also introduced, which may offer an alternative approach for multi-modal biometric systems.\n\n# Operating principle\nFirst the system has to localize the inner and outer boundaries of the iris (pupil and limbus) in an image of an eye.\nFurther subroutines detect and exclude eyelids, eyelashes, and specular reflections that often occlude parts of the iris.\nThe set of pixels containing only the iris, normalized by a [rubber-sheet model](https://bliptext.com/articles/rubber-sheet-model) to compensate for pupil dilation or constriction, is then analyzed to extract a bit pattern encoding the information needed to compare two iris images.\nIn the case of Daugman's algorithms, a [Gabor wavelet](https://bliptext.com/articles/gabor-filter) transform is used.\nThe result is a set of complex numbers that carry local amplitude and phase information about the iris pattern.\nIn Daugman's algorithms, most amplitude information is discarded, and the 2048 bits representing an iris pattern consist of phase information (complex sign bits of the Gabor wavelet projections).\nDiscarding the amplitude information ensures that the template remains largely unaffected by changes in illumination or camera gain, and contributes to the long-term usability of the biometric template.\nFor identification (one-to-many template matching) or verification (one-to-one template matching), a template created by imaging an iris is compared to stored templates in a database.\nIf the [Hamming distance](https://bliptext.com/articles/hamming-distance) is below the decision threshold, a positive identification has effectively been made because of the statistical extreme improbability that two different persons could agree by chance (\"collide\") in so many bits, given the high [entropy](https://bliptext.com/articles/entropy) of iris templates.\n# [A, now obsolete, IriScan model 2100 iris recognition camera | upload.wikimedia.org/wikipedia/commons/1/1b/IriScan///model///2100///iris///scanner///1.jpg]\n\n# Advantages\nThe iris of the eye has been described as the ideal part of the human body for biometric identification for several reasons:\nIt is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane (the [cornea](https://bliptext.com/articles/cornea)).\nThis distinguishes it from fingerprints, which can be difficult to recognize after years of certain types of manual labor.\nThe 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.\nThis makes the iris shape far more predictable than, for instance, that of the face.\nThe iris has a fine texture that—like fingerprints—is determined randomly during embryonic [gestation](https://bliptext.com/articles/gestation).\nLike the fingerprint, it is very hard (if not impossible) to prove that the iris is unique.\nHowever, 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.\nEven genetically identical individuals (and the left and right eyes of the same individual) have completely independent iris textures.\nAn iris scan is similar to taking a photograph and can be performed from about 10 cm to a few meters away.\nThere 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).\nThe commercially deployed iris-recognition algorithm, [John Daugman](https://bliptext.com/articles/john-daugman)'s IrisCode, has an unprecedented [false match](https://bliptext.com/articles/type-i-error) 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).\nWhile there are some medical and surgical procedures that can affect the colour and overall shape of the iris, the fine texture remains remarkably stable over many decades.\nSome iris identifications have succeeded over a period of about 30 years.\nIris recognition works with clear [contact lenses](https://bliptext.com/articles/contact-lens), [eyeglasses](https://bliptext.com/articles/eyeglasses), and non-mirrored [sunglasses](https://bliptext.com/articles/sunglasses).\nThe early Sensar technology worked by first finding the face, then the eyes, and then took the Iris images.\nThis was all done using infrared lighting.\nIt is possible to identify someone uniquely in a dark room while they were wearing sunglasses.\nMathematically, iris recognition based upon the original Daugman patents or other similar or related patents define the strongest biometric in the world.\nIris recognition will uniquely identify anyone, and easily discerns between identical twins.\nIf a human can verify the process by which the iris images are obtained (at a customs station, entering or even walking by an embassy, as a desktop 2nd factor for authentication, etc.) or through the use of live eye detection (which varies lighting to trigger slight dilation of the pupil and variations across a quick scan which may take several image snapshots) then the integrity of the identification are extremely high.\n\n# Shortcomings\nMany commercial iris scanners can be easily fooled by a high quality image of an iris or face in place of the real thing.\nThe scanners are often tough to adjust and can become bothersome for multiple people of different heights to use in succession.\nThe accuracy of scanners can be affected by changes in lighting.\nIris scanners are significantly more expensive than some other forms of biometrics, as well as password and [proximity card](https://bliptext.com/articles/proximity-card) security systems.\nIris 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.\nHowever, 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 Princeton Identity's \"Iris on the Move\" for persons walking at speeds up to 1 meter/sec).\nAs with other photographic biometric technologies, iris recognition is susceptible to poor image quality, with associated failure to enroll rates.\nAs 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.\nResearchers have tricked iris scanners using images generated from digital codes of stored irises.\nCriminals could exploit this flaw to steal the identities of others.\nThe first study on surgical patients involved modern cataract surgery and showed that it can change iris texture in such a way that iris pattern recognition is no longer feasible or the probability of falsely rejected subjects is increased.\n\n# Security considerations\nAs with most other biometric identification technology, an important consideration is live-tissue verification.\nThe reliability of any biometric identification depends on ensuring that the signal acquired and compared has actually been recorded from a live body part of the person to be identified and is not a manufactured template.\nBesides a person's physical characteristics, which includes the eyes, one's [voice](https://bliptext.com/articles/speaker-recognition) and [handwriting](https://bliptext.com/articles/handwriting-recognition) too, are not protected by the [Fourth Amendment](https://bliptext.com/articles/fourth-amendment-to-the-united-states-constitution) even though they are all constantly exposed.\nMany commercially available iris-recognition systems are easily fooled by presenting a high-quality photograph of a face instead of a real face, which makes such devices unsuitable for unsupervised applications, such as door access-control systems.\nHowever, this is not the case with all iris recognition algorithms.\nThe problem of live-tissue verification is less of a concern in supervised applications (e.g., [immigration control](https://bliptext.com/articles/immigration-control)), where a human operator supervises the process of taking the picture.\nMethods that have been suggested to provide some defence against the use of fake eyes and irises include changing ambient lighting during the identification (switching on a bright lamp), such that the [[pupil](https://bliptext.com/articles/pupil)lary reflex](https://bliptext.com/articles/[pupil](https://bliptext.com/articles/pupil)lary-reflex) can be verified and the iris image be recorded at several different [pupil](https://bliptext.com/articles/pupil) diameters; analysing the 2D spatial frequency spectrum of the iris image for the peaks caused by the printer [dither](https://bliptext.com/articles/dither) patterns found on commercially available fake-iris contact lenses; analysing the temporal frequency spectrum of the image for the peaks caused by computer displays.\nOther methods include using [spectral analysis](https://bliptext.com/articles/spectrometer) instead of merely monochromatic cameras to distinguish iris tissue from other material; observing the characteristic natural movement of an eyeball (measuring nystagmus, tracking eye while text is read, etc.); testing for retinal [retroreflection](https://bliptext.com/articles/retroreflection) ([red-eye effect](https://bliptext.com/articles/red-eye-effect)) or for reflections from the eye's four optical surfaces (front and back of both cornea and lens) to verify their presence, position and shape.\nAnother proposed method is to use 3D imaging (e.g., [stereo cameras](https://bliptext.com/articles/stereo-camera)) to verify the position and shape of the iris relative to other eye features.\nA 2004 report by the German [Federal Office for Information Security](https://bliptext.com/articles/federal-office-for-information-security) noted that none of the iris-recognition systems commercially available at the time implemented any live-tissue verification technology.\nLike any pattern-recognition technology, live-tissue verifiers will have their own false-reject probability and will therefore further reduce the overall probability that a legitimate user is accepted by the sensor.\n\n# Privacy considerations\n\n# Deployed applications\n# [IrisGuard Inc. UAE enrolment station | upload.wikimedia.org/wikipedia/en/d/d4/IrisGuard-UAE.JPG]\n# [IrisGuard Inc. First Cash Withdrawal on Iris Enabled ATM | upload.wikimedia.org/wikipedia/commons/f/f2/Iris///Recognition///Enabled///ATM.jpg]\n# [A U.S. Marine Corps Sergeant uses a \"PIER 2.3\" iris scanner to positively identify a member of the Baghdadi city council prior to a meeting with local tribal leaders, sheiks, community leaders and U.S. service members. | upload.wikimedia.org/wikipedia/commons/f/f6/USMC///Sergeant///identifies///Baghdaddi///city///council///member///with///iris///scanner.jpg]\n\n# Iris recognition in television and movies","revisionCount":0},"hasMore":false,"nextCursor":null}