Iris recognition
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Iris recognition is a method of biometric identification based on high-resolution images of the irides of an individual's eyes. Using a small camera, an iris-recognition system photographs one or both eyes and converts the small details in the iris stromal pattern into a bit pattern that is suitable for unambiguous positive identification of an individual.
Iris-recognition algorithms were pioneered by John Daugman (University of Cambridge Computer Laboratory), who holds a wide-ranging patent on the method. His IrisCode algorithm is the basis of all currently (as of 2006) commercially available iris-recognition system. These systems provide currently the lowest false-accept rate for any commercially available biometric-identification technology.
Operating principle
An iris-recognition algorithm first has to identify the approximately concentric circular outer boundaries of the iris and the pupil in a photo of an eye. The set of pixels covering only the iris is then transformed into a bit pattern that preserves the information that is essential for a statistically meaningful comparison between two iris images. The mathematical methods used resemble those of modern lossy compression algorithms for photographic images. In the case of Daugman's IrisCode, a Garbor wavelet transform is used in order to extract the spatial frequency range that contains a good best signal-to-noise ratio considering the focus quality of available cameras. The result are a set of complex numbers that carry local amplitude and phase information for the iris image. In IrisCode, all amplitude information is discarded, and the resulting 2048 bits that represent an iris consist only of the complex sign bits of the Garbor-domain representation of the iris image. Discarding the amplitude information ensures that the IrisCode remains largely unaffected by changes in illumination and iris colour, which contributes significantly to the long-term stability of the code. To verify an IrisCode, its Hamming distance to a previously recorded IrisCode has to be below a suitable selected threshold.
A practical problem of iris recognition is that the iris is usually partially covered by eye lids and eye lashes. In order to reduce the false-reject risk in such cases, additional algorithms are needed to identify the locations of eye lids and eye lashes, and exclude the bits in the resulting code from the comparison operation.
Advantages
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 labour.
- The iris is mostly flat and its geometric configuration is only controlled by a single muscle, which controls the diametre 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 embrionic gestation. Even genetically identical individuals have completely independent iris textures, wereas DNA (genetic "fingerprinting") is not unique for the about 1.5% of the human population who have a genetically identical monozygotic twin.
- 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 to be 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 finger-print scanners, where a finger has to touch a surface, or retinal scanning, where the eye can be brought very close to a lens (like looking into a microscope lens).
- Some argue that a focused digital photograph with an iris diameter of about 200 pixels contains much more long-term stable information than a fingerprint. In practice, such a generalization depends specifically on the metrics used.
- The only currently commercially deployed iris recognition algorithm, John Daugman's IrisCode, has an unprecedented false match rate. Not a single false match has ever been reported for this algorithm, which has already been used to compare more then 200 billion iris pairs as part of the immigration procedures in the United Arab Emirates.
- While 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. Some iris identification have succeeded over a period of about 30 years.
Disadvantages
- 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 finger-print 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.
- 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.
Security considerations
Like with most other biometric identification technology, a still not satisfactorily solved problem with iris recognition is the problem of "live tissue verification". The 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. Many 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. The problems of live tissue verification is less of a concern in supervised applications (e.g., immigration control), where a human operator supervises the process of taking the picture.
Methods 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 can be verified and the iris image be recorded at several different pupil diameters
- Analysing the 2D spatial frequency spectrum of the iris image for the peaks caused by the printer 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
- Using spectral analysis 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 coaxial retinal back-reflection ("red-eye" effect)
- Testing for reflections from the eye's four optical surfaces (front and back of both cornea and lens) to verify their presence, position and shape
- Using 3D imaging (e.g., stereo cameras) to verify the position and shape of the iris relative to other eye features
Deployed applications
- One of three biometric identification technologies internationally standardized by ICAO for use in future passports (the other two are fingerprint and face recognition)
- At Schiphol Airport, Netherlands, iris recognition has permitted passport free immigration since 2001
- United Arab Emirates border control at all 17 air, land and seas ports since 2001
- ...
Iris recognition in fiction
- Steven Spielberg's 2002 science fiction film Minority Report depicts a society in which what appears to be a form of iris recognition has become daily practice. A main character has an eye transplant in order to change his identity.
References
- Leonard Flom, Aran Safir: Iris recognition system. International patent WO8605018A1, 28 August 1986.
- John Daugman: Biometric personal identification system based on iris analysis. U.S. Patent No. 5,291,560, 1 March 1994.
- John Daugman: [How iris recognition works]. IEEE Transactions on Circuits and Systems for Video Technology 14(1), January 2004, pp 21–30
- John Daugman: The importance of being random: statistical principles of iris recognition. Pattern recognition 36, 2003, pp 279–291.
- John Daugman: [Results from 200 billion iris cross-comparisons]. Technical Report UCAM-CL-TR-635, University of Cambridge Computer Laboratory, June 2005.
External links
- [Home page of IrisCode designer John Daugman]
- [Interview with John Daugman]
- [NIST Iris Challence Evaluation] – a contest for competing iris-recognition algorithms
- [Jan Ernst's iris recognition page]
- [Anti-iris-recognition activist's web site]
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