当前位置：文档库 › Iris Recognition Systems and methods

Iris Recognition Systems and methods

Jarkko Vartiainen

Lappeenranta University of Technology,

Department of Information Technology

P.O.Box20,

53851Lappeenranta,Finland

vartiain@lut.fi

Abstract.This is an overview of the new emerging biometric technology

called iris recognition.The focus will be on image processing and security

aspects.The most known algorithms are introduced and discussed and

background for iris recognition are given.Results will show that iris

recognition is very good biometric that is comfortable to use for person

identi?cation.

1Introduction

Iris recognition is a part of biometric identi?cation methodoligies which also include facial,?ngerprint,retinal and many other biological traits.They all o?er a new solutions for person identi?cation,authentication and security.Currently users have to carry security badges or know certain pin/pass codes in order to get into secure zones or to log into a computer.Problem with these methods is that users have to remember lots of di?erent passwords and pincodes which therefore tend to be rather easy to guess and crack since users prefer passwords that are easy to remember.Cards can be lost and they can be used by anyone to gain access to a restricted area or to a restricted computer.Biometrics on the other hand provide a certain and easy way of authenticating persons,biometrics are also quite hard to forge and combined with some other method like password they form up a very strong authentication method.

Biometric identi?cation utilises many psychological and physical characteris-tics that de?ne us as a individual.Some more common features are?ngerprints, hand shapes,eyes retinas and many others,including eye’s iris.Psychical and behavioural characteristics include for example typing speed,walking style and signature.Out of all physiological properties iris patterns are believed to be one of the most accurate.[4]

Iris recognition is in many ways a very good research topic in computer science.It combines many aspects of information technology research,such as computer vision,pattern recognition,statistics and human-machine interface. The purpose of iris recognition is real-time,high con?dence recognition of a per-son’s identity by mathematical analysis of the random patterns that are visible within the iris of an eye from some distance.Iris recognition has many practi-cal uses,it can be used to authenticate persons identity or to identify a certain person from a large set of data.

2Background

Iris identi?cation methods are quite new in the computational world.The idea that people can be identi?ed by the shape of their irises was?rst documented in an iophthalmology textbook by James Doggarts in1949.After that the idea lie dormant for decades until1987two ophthalmologists,Aran Sa?r and Leonard Flom,patented this idea.In1989they asked John Daugman to create an actual algorithm for the iris recognition problem.These algorithm that he developed and patented in1994form all the basis for the current iris recognition research and products.[7]

There has always been interest for the human iris and it has been studied very long time by iridologists.Iridology resembles palm-reading,thus it has no scienti?c background.Iridologists claim that they can see just by looking at a persons iris the state of his inner organs,health and even his personality.This all was declared as a medical fraud by?ve di?erent medical journal publications. But as the iridilogists prove,the iris has been in the interest of man long before it became interesting scienti?cally for computer scientists.

3Physiology of the iris

3.1The Iris

The iris is a protected internal organ of the eye,located behind the cornea and the aqueous humour,but in front of the lens(see?g.1).The iris has many features that can be used to distinguish one iris from another.One of the pri-mary visible characteristic is the trabecular meshwork,a tissue which gives the appearance of dividing the iris in a radial fashion that is permanently formed by the eighth month of gestation.During the development of the iris,there is no genetic in?uence on it,a process known as chaotic morphogenesis that occurs during the seventh month of gestation,which means that even identical twins have uncorrelated minutae,i.e.di?ering irises.In fact,even persons own eyes are uncorrelated.Pigmentation of the iris on the other hand still continues on the?rst year after birth.It is usual that new born baby has blue eyes,but after ?rst year the babys eyes may have changed colour.What makes the iris also interesting is the fact that subjects iris is rather easy to photo from a distance in unintrusively and perhaps even inconspicuously.

The most important function of the iris is controlling the size of the pupil. Illumination,which enters the pupil and falls on the retina of the eye,is controlled by muscles in the iris.They regulate the size of the pupil and this is what permits the iris to control the amount of light entering the pupil.The change in the size results from involuntary re?exes and is not under conscious control.This feature can be used to guarantee that the image being taken is probably with a high con?dence a living eye and not an arti?cial image of an eye.[10,4]

Fig.1.The structure of the human eye.

3.2Features of the iris

Among the pigment related features belong the crypts and the pigment spots and naturally the color of the iris.Crypts are noticeable thin lines that extend from the pupil to the edges of the iris.Pigment spots are random concentrations of pigment cells in the visible surface of the iris.They are known as moles and freckles with nearly black colour.Features that control the actual size of the pupil are called radial and concentric furrows.Together they are called contraction furrows and they control the size of the pupil,which in turn controls how much light gets into the eye.[14]

Typical radial furrows usually begin near the pupil and extend through the collarette.The radial furrows are creased in the anterior layer of the iris,from which loose tissue may bulge outward and this is what permits the iris to change the size of the pupil.The concentric furrows are generally circular and concentric with the pupil.They typically appear in the ciliary area,near the periphery of the iris and permit to bulge the loose tissue outward in di?erent direction than the radial furrows.Collarette,mentioned brie?y above,is the boundary between the ciliary area and the pupillary area.It is a sinuous line as can be seen from ?gure3,which forms an elevated ridge running parallel with the margin of the pupil.The collarette is the thickest part of the human iris.[14] The most striking part of the iris if of course the pupil,black round dot in the middle of the iris as can be seen in?gures2and3.Pupil may at?rst glance seem round in shape,but in actuality it may not be exactly circular in shape and its deviation from the circle is a visible characteristics.Centers of the iris and the pupil are di?erent and they can di?er from each other of about20%.

All previously mentioned radial and angular variations taken together con-stitute a distinctive identity that can be imaged from some distance.Further properties of the iris that enhance its suitability for use in high con?dence iden-ti?cation systems include its inherent isolation and protection from the external environment.Humans protect their eyes instinctly since they are the most valu-able of human senses.Irises are also impossible to be surgically modi?ed without

Crypt

Radial furrows

Collarette

(dottet line)Fig.2.Visible features of the eye.

unacceptable risk to vision and iris physiological response to light provides one of several natural tests against arti?ce.Only rarely the iris texture changes,this is usually due to aging or trauma to the eye,in which case atrophic areas may appear in the iris resulting in a “moth-eaten”texture.Tumours may grow on the iris,or congenital laments may occur connecting the iris to the lens of the eye.[14,8,2]

4Iris recognition

The work of John Daugman is considered as the foundation that all current research is more or less based on.Also all of the commercial applications that are currently in the markets are based on his method [11].Before Daugman started to tackle with the problem,there were no studies made whether or not irises have su?cient degrees-of-freedom,or forms of variation in the iris among individuals,to use them as a “?ngerprints”to distinguish di?erent persons.It was also unknown whether or not an e?cient enough algorithm could be developed to extract a detailed iris images from video image.Also the iris code should be short enough and yet mathematically varying enough that it would be able to render a decision about individual identity with high statistical con?dence.And all that should also happen in less than one second of computational time with current general purpose micro processor.[2]

4.1Daugman’s method

In Daugman’s work the visible texture of a person’s iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coe?cients,whose most-signi?cant bits comprise a 256-byte iris code.The iris recognition procedure is a three step process.First image of the eye is

Crypts

Concentric furrow (dotted)

Fig.3.More features of the iris.

captured by using a standard digital video camera.Then from the image,eye and iris are located and?nally iris code is calculated and compared to the database. [2,17]

Image acquisition Iris analysis begins with reliable means for detecting whether an iris is visible in the video and then precisely locating its inner and outer boundaries.This is done by utilizing the fact that iris is round in shape and thus by integration and di?erentation needs to be applied in order to?nd the correct location.This is accomplished by maximizing the blurred partial derivative,with respect to increasing radius r,of the normalized contour integral of the image along a circular arc of radius r and the iris center coordinates.The complete operator behaves in e?ect as a circular edge detector.[2]

After the iris roughly located a second search?nds the fainter pupillary boundary by using a?ner convolution scale and smaller search range.The end result is the precise location of the outer boundaries of an iris and the pupillary boundary.Facts like the knowledge that screla is always lighter than the iris are also used to make the algorithm more precise.There are also di?culties like the fact that the pupil isn’t always darker than the iris.That problem is resolved by using the absolute value of the partial derivative.This increases the performance of the operator as circular edge detector regardless of these polarities.Also by using near infrared illumination,darker irises will show more details and also user does not see the light which makes it unintrusive.[2]

The?nal codes that will represent the iris have to be extracted from corre-sponding areas of iris texture.The same regions of the iris need to be tested for similarity.Scaling and the overall iris image can vary due to pupillary contraction

or di?erence in the camera distance.This problem is cicumvented through the use of a projected polar coordinate system and by modelling the irirs as a non-elastic rubber sheet.This model assigns a pair of dimensionless real coordinates (radius,angle)to each point of the iris.[2,4]

After this mapping zones of analysis are de?ned in this projected doubly dimensionless coordinate system.These zones disregard the top of the iris(be-cause of eyelid coverage)as well as the area where the light source coming from below causes a corneal re?ection.The illumination comes from an angle,even if it causes re?ections,because it helps avoiding in?uence of sunglasses.[2] Feature extraction After the pupillary and iris/screla boundaries have been located,any occluding eyelids detected and re?ections or eyelashes excluded, the isolated iris is mapped to size-invariant coordinates and demodulated to extract its phase information using2D Gabor Wavelets.Daugman introduced these?lters in1980.Their mathematical properties include the ability of pro-viding high-resolution information about the orientation and spatial frequency content of the image structure.The demodulation process is shown in?gure

Fig.4.The phase demodulation process used to encode iris patterns.The angle of each projection phasor is quantized to its quadrant,setting two bits of phase information. This process is repeated all across the iris with many wavelet sizes,frequencies,and orientations to extract2048bits.

It amounts to a patch-wise phase quantization of the iris texture,by identi-fying in which quadrant of the complex plane each resultant phasor lies when a given area of the iris is projected onto complex-valued2D Gabor wavelets:

h{Re,Im)}=sgn{Re,Im)}

I(ρ,φ)e?iω(θ0?φ)e?(r0?ρ)2/α2e?(θ0?φ)2/β2ρdρdφ

(1)

where h{Re,Im)}can be regarded as a complex-valued bit whose real and imaginary parts are either0or1depending on the sign of the2D integral. I(ρ,φ)is the raw iris image in a dimensionless polar coordinate system that is size-and translation-invariant and also corrects for pupil dilation.αandβare the multi-scale2D wavelet size parameters.They span an8-fold range from 0.15mm to1.2mm on the iris.ωis wavelet frequency,spanning3octaves in inverse proportion toβ.(r0,θ0represent the polar coordinates of each region of iris for which the phasor coordinates h{Re,Im)}are computed.Altogether2048 phase bits(256bytes)are calculated for each iris.The number of bytes was chosen according to the capacity of the three channel magnetic stripe of the standard credit cards.This also happens to be the upper bound of the capacity of the iris information.[2,4]

Only phase information is used in the IrisCode(TM)(phase code)since am-plitude information is not very discriminating and it depends on exrtaneus in-formation like image contrast,illumination and camera gain.The extraction of phase has also the advantage that phase angles are assigned regardless of how poor the image contrast is as with very poorly focused images.[2,4]

Pattern recognition The key to iris recognition is the failure of a test of sta-tistical independence,which involves so many degrees-of-freedom that this test is virtually guaranteed to be passed whenever the phase codes for two di?erent eyes are compared,but to be uniquely failed when any eye’s phase code is com-pared with another version of itself.This way the problem of pattern recognition is converted to a simple statistical test of independence.In order to reach the recognition result the Hamming Distance of the code of the new iris and all the stored codes is calculated.A simple XOR operation between the corresponding pair of codes provides this Hamming Distance:

HD= (codeA?codeB)∩maskA∩maskB

maskA∩maskB

(2)

Hamming code measures the dissimilarity between two irises whose phase code bit vectors are denoted{codeA,codeB}and whos mask bit vectors are denoted{maskA,maskB}.The denominator tallies the total number of phase bits that mattered in iris comparisons after artifacts such as eyelashes and spec-ular re?ections were discounted.The resulting HD is a fractional measure of dissimilarity.In the original work Daugman did not use mask bit vectors,later he improved the original algorithm by including mask bit vectors.Masking bits signify whether any iris region is obscured by eyelids,contains any eyelash oc-clusions,specular re?ections,boundary artifacts of hard contact lenses,or poor signal-to-noise ratio and thus should be ignored in the phase code as artifact.[4, 6]

Because any given bit in the phase code for an iris is equally likely to be1 or0,and di?erent irises are uncorrelated,the expected proportion of agreeing bits between the codes for two di?erent irises is HD=0.500.Figure5(a)shows

the distribution HDs obtained from9.1million iris pair comparisons.The ob-served mean HD was p=0.499with standard deviationσ=0.0317.Their full distribution corresponds to a fractional binomial having N=p(1?p)/σ2=249 degrees-of-freedom as can be seen from the solid curve.This shows that each comparison between two phase codes bits from two di?erent irises is essentially a Bernoulli trial even though there are correlations among the“coin tosses.”

In any given IrisCode,only small subsets of the code are mutually inde-pendent due to the internal correlations within iris.Bernoulli trials still remain binomially distributed but with a reduction in N and thus increasing the devi-ation of the normalized HD distribution.The theoretical binomial distribution plotted in the?gure5(a)has the fractional form:[4]

f(x)=

m!(N?m)!

p m(1?p)(N?m)(3)

where N=249,p=0.5,and x=m/N is the outcome fraction of N Bernoulli trials.

(a)(b)

Fig.5.a)Distribution of Hamming Distances from all9.1million possible comparisons between di?erent pairs of irises in the database.The histogram forms a perfect bino-mial distribution with p=0:5and N=249degrees-of-freedom,as shown by the solid curve(Eqt4).The data implies that it is extremely improbable for two di?erent irises to disagree in less than about a third of their phase information.b)Decision environ-ment for relatively unfavourable conditions.Darker histogram shows the distribution of shortest HDs from all9.1million possible comparisons after7possible rotations.

What is also remarkable in the work is the fact that irises that are genetically identical have the same distribution of Hamming distances than non correlated

irises.Meaning that persons left and right eye have totally di?erent irises,which also means that identical twins have also totally di?erent irises.[4] Next question that hasn’t yet been answered is that how the algorithm copes with the fact that head can be tilted from one side or the other while the iris is being imaged.The solution is to“rotate”the IrisCode within certain limitations and then make the identi?cation comparison again.After the comparisons only the code that produces the shortest HD is chosen as the matching IrisCode. Even after allowing for7di?erent degrees of eye or head tilt the distributions of hamming distances the distribution is only slightly biased toward a lower mean Hamming distance5(b).The lighter bargraph in?gure5(b)shows the same distribution between pairs of di?erent iris codes given for each given iris allowing again7di?erent degrees of eye or head tilt.As can be seen from the?gure, the di?erence between iris comparisons even after some degrees of freedom are still quite clearly https://www.wendangku.net/doc/b43257227.html,parison of matching irises and di?erent irises have distinctive di?erence.Of the9.1million di?erent iris comparisons plotted as dark histogram in?gure5(b)the smallest HD that was measured by Daugman was 0.334.The number means that only2/3(66%)of the IrisCode bits matched when comparing irises from di?erent eyes.The binomial cumulative from0to0.300is 1in10billion which is roughly the number of human eyes on the planet.Thus even the observation of a relatively poor degree of match between IrisCodes for two di?erent iris images(say70%agreement or HD=0.300)would still provide compelling evidence of identity because the test of statistical independence is still failed so convincingly.[5]

Table1.False match probability as a function of decision criterion.

HD criterion Odds of false match

0.261in1013

0.271in1012

0.281in1011

0.291in13billion

0.301in1.5billion

0.311in185million

0.321in26million

0.331in4million

0.341in690000

0.351in133000

0.361in28000

0.371in6750

0.381in1780

0.391in520

0.401in170

The statistical data and theory presented above show that iris recognition can be performed successfully by just a test of statistical independence.Any

two di?erent irises will most probably pass the test of statistical independence whilst any two images that fail this test(i.e.produce HD≤0.32)must be from the same iris.Thus it is the unique failure of the test of independence that is the basis for iris recognition.Table1shows the probabilities of false acceptance rates based on di?erent HD criterion.[6]

There is another quantative way to calibrate the power of decision making for this type of two-choice task by using a metric called d .Decidebility index measures how well separated the two distributions are,since recognition errors are caused by their overlap.If the means of the distributions areμ1andμ2and their standard deviationsσ1andσ2,then d is de?ned as

d =

|μ1?μ2|

(σ21+σ22)/2

(4)

This measure of decidibility is independent of how liberal or conservative is the acceptance threshold used.By measuring separation it re?ects the degree to which any improvement in the false match error rate must be paid for by worsening of the failure to match error rate.The measured decidibility for iris recognition is d =7.3for the non-ideal condition presented in?gure5(b).The value is higher than any other biometric has ever achieved.[6,3] These extremely good statistical properties make iris recognition such a good identi?cation method.Veri?cation(one-to-one comparison)as a process is much more simpler than identi?cation(one-to-many comparison).For example if veri?-cation method that has99.9%success rate,is used for identi?cation in a database of size2000,then the veri?cation method makes a false acceptance in86%of the cases.Iris recognition on the other hand can adapt the HD threshold and can thus always keep the false accept rate nearly a constant regardless of database size.[5]

It is no wonder that all the current commercial iris recognition systems are based on Daugmans work.The algorithm is robust and extremely e?ective and it has more uses than mere veri?cation,it can be also used for identi?cation which means that no ID badges or keycards are required for gaining access to sensitive areas or information.The iris recognition system patented by Daugman has not failed a single test of acceptance to date even though it has been tested quite exhaustively even with a database of size984million template pairs(Test was carried out by Iridian Technologies in2003,the report is available in their website after registration).

4.2Other methods

There are also other researchers in the iris recognition?eld even though Daug-man is the best known.Wildes et al.[16]introduce a system which di?ers a bit from Daugmans.First,their system?nds the eye of the subject without giving the subject any special feedback like in Daugmans system,where the user sees his eye constantly in a monitor and must move his head in order to get a clear image.Also Wildes light source is di?used and polarized whilst Daugman uses

near infrared point light source.Polarization allows Wildes system to ameliorate the e?ects of specular re?ections in the iris imaging.

Feature extraction in Wildes system is based on an isotropic bandpass de-composition derived from the application of Laplacian of Gaussian?lters to the image data.Matching the obtained and the stored iris representations is based on normalized correlation(NC)between both representations.Let p1[i,j]and p2[i,j]be the two images arrays of size n×m and letμ1,μ2andσ1,σ2be their means and standard deviations.Then the normalized correlation between p1and p2can be de?ned as

NC=σn i=1σm j=1(p1[i,j]?μ1)(p2[i,j]?μ2)

nmσ1σ2

(5)

the Laplacian pyramid representations instantiate four spatial frequency bands, so four scores are obtained,each accounting for the goodness of match at each frequency band.Finally,it is necessary to combine these four opinions into a sin-gle?nal decision.In an opinion fusion scenario,Wildes chooses to use a Fisher’s linear discriminant,applying a threshold afterwards.The weakness of Wildes method is that it’s computationally very expensive since it relies on image reg-istration and image matching and most of all it can only be used for veri?cation and not identi?cation.[16]

Another researcher[1]has also worked with iris recognition.His approach is based on calculating the zero crossings of the wavelet transform.2002,In his work apart from the iris location a normalization algorithm brings the iris to have the same diameter and the same number of data points.From the grey levels of the sample images,one-dimensional signals are obtained and referred to as the Iris signature.Then a Zero Crossings representation is calculated based on the Wavelet Transform.These representations are stored as templates and are used for the matching algorithm.In this way the author claims that the noise in?uence will be eliminated since zero crossings are not a?ected by noise. Interesting aspect in his work is the ability of the wavelet transform to eliminate the e?ect of glares due to re?ection of the light source on the surface of the iris. This was a problem that neither Daugman nor Wildes were able to solve.Also he tested various resolutions and chose the most signi?cant levels which contained most of the energy of the iris signature and thus were less a?ected by noise.[1] After locating the pupil with the assumption that it composed a circular closed contour the centroid of the pupil is chosen as a point of https://www.wendangku.net/doc/b43257227.html,ing this reference,concentric circles are formed to collect data in circular bu?ers. From each bu?er an“Iris signature”is generated.This procedure needs an addi-tional normalization process since the diameter of the iris could vary.In order to achieve this,the maximum diameter is chosen as reference and is used to scale the virtual circle diameters to constant size.Normalization also takes place with the data points.Since the same number of points are needed,a normalization value is selected to help the wavelet transform to extract all the information available in the iris signature.[1]

In order to reach the recognition result,models of iris signatures are made from all the signatures using the same normalization constants for both the users irises as well as the testing https://www.wendangku.net/doc/b43257227.html,ing the number and the location points of the zero-crossings,a dissimilarity measure is obtained.The iris with the minimum value is chosen as the correct target.The dissimilarity value is the average of the respective dissimilarity values at the various resolution levels.The?rst two are calculated using all the data points and the?nal two using only the zero-crossings.However,further processes are needed to compensate for di?erent number of zero-crossings.[1]

There are also other methods[11,13,12]that have been invented,but in general it can be said that they all are inferior in comparison against Daugmans method.There is still room for improvement in the?eld and all the methods given here need improvement before the system is ready to be used very widely. 5Reliability of iris recognition

5.1Possible ways to trick iris recognition systems

There are several ways to con?rm that a living iris is being scanned and not for example a photograph,a videotape,or a fake iris printed on a contact lens, glass or other arti?ce.One way is of course to measure the ratio of pupil diam-eter against to iris diameter either when light levels are changing or even under steady illumination.The pupil size can be controlled by preprogrammed ran-dom changes in the light level with a response time constant of about250ms for constriction and about400ms for dilation.But even without programmed illu-mination changes,the disequilibrium between excitatory and inhibitory signals from the brain stem to the enervation of the pupillary sphincter muscle pro-duces a steady-state small oscillation at about0.5Hz termed hippus.Since the algorithms usually has to track both the pupillary boundary and the iris bound-ary,it is routine to monitor the amount of hippus.Its coe?cient of variation is normally at least3%.[5]

Other ways to exclude a photograph of somebody else’s iris involve tracking eyelid movements,or examining corneal re?ections of infrared LEDs illuminated in random sequences.Still further measures could test for the characteristic spec-tral signature of living tissue in infrared illumination.Hemoglobin in oxygenated blood has an absorption band in the near infrared wavelengths,whereas printer’s dyes and emulsions and re?ectance properties of photographic papers are often completely ine?ective for infrared light.[5]

Finally a person can try to fool the iris recognition system with a contact lenses with faked iris pattern printed on them.The fact that such a fake iris is?oating on the spherical,external surface of the cornea,rather than lying in an internal plane within the eye,lends itself to optical detection.Also,the printed iris pattern doesn’t undergo any distortions when the pupil changes in size,as does a living iris pattern.Moreover,the printing process itself creates a characteristic signature that can be detected,as can be seen in?gure6.The

image shows a natural iris and a fake one printed onto a contact lens and their 2D Fourier power spectra.The dot matrix printing process generates four points of spurious energy in the Fourier plane,corresponding to the directions and periodicities of coherence in the printing dot matrix,whereas a natural iris does not have these spurious coherences.[5]

There are some problems with the iris recognition too,the object to be tracked is very small within moving target and is obscured by eyelashes,lenses and re?ections.Iris is located behind a curved,wet re?ecting surface and it also deforms non-elastically,which make the pattern tracking rather challenging task. Illumination should also not be too visible or bright or it annoys the users and eventually when technology goes far enough it o?ers some negative Orwellian connotations since every people could be rather easily tracked by their eyes, since we have to use them for almost everything.[9]

5.2Testing of some commercial iris recognition system

Since biometrics are becoming more and more general,a German research group tried to defeat several biometric systems by means that are widely available.Iris recognition system was thought to be the hardest to defeat.First they tried to o?er high resolution images of an iris via notebook notebook pc display as well as via a head-mounted display with no success.Also regular paper o?ered no results,but by looking at the images the system took,they noticed a bright spot in the middle of the pupil which gave them an idea.They cut a small hole in the middle of the paper and the printed the iris image on that same mat paper with resolution of2400×1200dpi.Then they tried to log into a system with the image by covering a living eye with the image so that a living pupil was in the middle of the image(?gure7).This was enough to convince the system that a living and authentic person was trying to access the system and granted access under the assumed identity of“Master False Eye”.[15]

They also tried to enrol into the system with the aid of an“arti?cial”eye. From that point onward,anyone with the possession of the eye pattern was able to log on to the system.Moreover,the person whose eye had been used to create the pattern was also able to acquire authentication in relation to the picture-generated reference data set with his own live iris.[15]

It has to be said if favor of the iris scanner that under real life conditions it would be hard to obtain iris images of authorized persons with high enough accuracy.But should such an image be available,then creating a deceptive eye patch would not be much of a problem since high resolution ink jet printers and good quality papers are widely available.So even if in theory a living is easy to identify,the commercial systems still need some work before they can be used for real security purposes.[15]

6Summary

When we need to know with certainty who an individual is or whether he is who he claims to be,we normally rely upon what he possesses like a key or a card or

Fig.6.Illustration of one countermeasure against subterfuge:detecting a printed eye on a contact lens by the2D Fourier plane artifacts of printing.

Fig.7.Achieving authentication with someone else’s iris by hiding your own pupil behind it.

we rely on the fact that he knows something that nobody else knows,like a pass-word or a pincode.Other way to identify a person is to use a unique biological trait,something which is unique from person to person,like a?ngerprint or ap-pearance.The?rst two methods are easy to implement technologically and easy to con?rm automatically,but they are also very unreliable since everyone can use password or key without proper authorization.Today we know,that person uniqueness is determined by his genes,but DNA testing is not unintrusive nor fast and thus is not acceptable in everyday usage.The remaining options are characteristics that are unique for every person regardless of aging and other aspects.Iris recognition o?ers very reliable way to recognize and identify per-sons.Iris is well protected,immutable,internal organ of the eye,that is readily visible externally and has very random patterns from person to person.This identi?cation power,which means that users need not even bother to assert an identity,is one of the main advantages of iris recognition as a biometric. References

1.W.W.Boles.A security system based on human iris identi?cation using wavelet

transform.Engineering Applications of Arti?cial Intelligence,11(1):77–85,Feb.

1998.

2.J.Daugman.High con?dence visual recognition of persons by a test of statistical

independence.IEEE transactions on Pattern Analysis and Machine Intelligence, 15(11):1148–1161,Nov.1993.

3.J.Daugman.Iris recognition.American Scientist,89(4):326–333,July-Aug.2001.

4.J.Daugman.How iris recognition works.In2002International Confrence on

Image Processing,volume1,pages33–36,Sept.2002.

5.J.Daugman.Demodulation by complex-valued wavelets for stochastic pattern

recognition.International Journal of Wavelets,Multi-resolution and Information Processing,1(1):1–17,2003.

6.J.Daugman.The importance of being random:Statistical principles of iris recog-

nition.Pattern Recognition,36(2):279–291,2003.

7.J.Daugman.History and development of iris recognition.[html-doc.],[retrieved

10.10.2003].From:https://www.wendangku.net/doc/b43257227.html,/users/jgd1000/history.html.

8.J.Daugman.Anatomy and physiology of the iris.[htlm-doc.],[retrieved

15.10.2003].From:https://www.wendangku.net/doc/b43257227.html,/users/jgd1000/anatomy.html.

9.J.Daugman.Advantages of the iris for identi?cation.[html-doc.],[retrieved:

24.10.2003.From:https://www.wendangku.net/doc/b43257227.html,/users/jgd1000/addisadvans.html. 10.J.Daugman.Anatomy and physiology of the iris.[html-doc.],[retrieved:

24.10.2003].From:https://www.wendangku.net/doc/b43257227.html,/users/jgd1000/anatomy.html.

11.Y-P.Huang,S-W.Luo,and E-Y.Chen.An e?cient iris recognition system.In

Proceedings of the First International Conference on Machine Learning and Cy-bernetics,volume1,pages450–454,Nov.2002.

12.Ma L.,Y.Wang,and Tan T.Iris recognition using circular symmetric?lters.

In Proceedings.16th International Conference on Pattern Recognition,volume2, pages414–417,Aug.2002.

13.L.W.Liam,A.Chekima,L.C.Fan,and J.A.Dargham.ris recognition using self-

organizing neural network.In SCOReD2002.Student Conference on Research and Development,pages169–172,July2002.

14. A.Muroˇn and J.Posp′?ˇs il.The human iris structure

and its usages.[html-doc.],[retrieved15.10.2003].From: http://publib.upol.cz/～obd/fulltext/Physica39/Physica3907.pdf.

15.L.Thalheim,J.Krissler,and R.W.Ziegler,P-M.Translated by Smith.Body

check:Biometrics defeated.[www-article],May[retrieved:24.10.2003].From: http://www.heise.de/ct/english/02/11/114/.

16.R.P.et al.Wildes.A system for automated iris recognition.In Proceedings of the

Second IEEE Workshop on Applications ofComputer Vision,pages121–128,Dec.

1994.

17.G.Williams.Iris recognition technology.IEEE Aerospace and Electronic Systems

Magazine,12(4):23–29,April1997.