Iris recognition border-crossing system in the UAEjgd1000/UAEdeployment.pdf · The 420,000 registered UAE expellees represent more than 180 nationalities, and, of course, the 2,128,300

Over a distributed network involving all17 air, land, and sea ports into theEmirates, the iris patterns of all arriving

passengers are compared in real-time exhaustivelyagainst an enrolled central database. Accordingto the Ministry of Interior which controls thedatabase, so far not a single False Match has beenmade, despite some 2.7 billion iris cross-comparisons being done every day.

On a typical day, more than 6,500 passengersenter the UAE via seven international airports,three land ports, and seven sea ports. By looking atan iris camera for a second or two while passingthrough immigration control, each passenger's irispatterns are encoded mathematically and the

resulting IrisCodes sent over a distributedcommunications network to a central databasecontrolled by the General Directorate of AbuDhabi Police. There they are compared exhaustivelyagainst an enrolled database of 420,000 IrisCodesof persons who were expelled from the UAE forvarious violations, many of whom make repeatedefforts to re-enter the UAE with new identitiesusing forged travel documents. Thus the current

daily number of iris cross-comparisons performedunder the UAE expellee tracking and border-crossing control system is about 2.7 billion. It is thefirst system of its kind in the world, with more than2.1 million arriving passengers already checked inthis way. The time required for each passenger to becompared against the full database of registeredIrisCodes is less than one second. So far more than9,500 persons have been caught by this system,travelling with forged identities. According to Lt.

Col. Ahmad Naser Al-Raisi, Director of theInformation Technology Department at theGeneral Directorate of Abu Dhabi Police, ‘‘Wefound the system to be very effective and extremelyfast. Its speed, accuracy, and ease-of-use enabled usto deploy the project without difficulties.’’

The UAE national iris recognition system is asynthesis of three core components: iris cameraswith autofocus and autozoom, developed by LG;the iris recognition algorithms, developed by JohnDaugman of Cambridge University; and anetworked distributed server and communicationsarchitecture called ‘IrisFarm’, developed by Imad

Reproduced from International Airport Review, Issue 2, 2004To subscribe contact Katie Barwick on +44 (0)1959 563311 or email [email protected]

BIOMETRICS

Iris recognition border-crossing systemin the UAE❖ John Daugman OBE, University of Cambridge and Imad Malhas, President and CEO, IrisGuard Inc.

The largest national deployment so far of irisrecognition – the automatic recognition of personsby the complex patterns visible in the irises of theireyes – is now in its third year of operation in theUnited Arab Emirates (UAE).

Figure 1: Exampleof a human iris,illustrating thecomplexity andrandomness ofthese uniqueidentifying patterns

Figure 2: Deployment map of the fully networked IrisRecognition System covering all 17 air, land, and seaports into the United Arab Emirates

Malhas of IrisGuard Inc. It allows simultaneousenrollments into the central database withoutinterrupting parallel search queries from multipledistributed stations; and it offers almost unlimited

scalability to national populations of registeredpersons and travellers without reduction inexecution speed. Critical features of the IrisFarmarchitecture include:● Ability to split the full national database of

IrisCodes into an unlimited number of parallelsearch engines (‘IrisEngines’). Each searchengine can perform more than 500,000 completeiris comparisons per second

● Support of a diverse communications network,including very slow links. Even links as slow as33.6 KBytes/sec are compatible with real-timeperformance on iris recognition requests

● Automatic synchronisation of IrisCode datafrom detached enrollment locations

● Uninterrupted search capability duringsynchronisation

● Use of separate hardware components forenrollment, central database maintenance, andrecognition functions. This maintainspredictable performance and allows load-sharing, distributing the computing workload tomatch demand with available resources andproviding resilience against faults

● Adaptive modularity, allowing hardware to beadded to support any requirements for responsespeed or database size

● Automatic backup procedures, together with‘hot standby’ capability to switch the entireFarm to a second emergency location inmission-critical applications

Although this system was designed to preventillegal immigrants and former expellees fromentering a country using fraudulent traveldocuments, by comparing the iris biometric of allarriving passengers against a ‘negative watch list’of detainees, all aspects of the IrisFarmarchitecture, cameras, and the core iris recognitionalgorithms are equally suited for ‘positive’applications in which the main goal is to enhancethe convenience, speed, and efficiency of border-crossing formalities for legitimate travellers. Suchdeployments (using the same Daugman algorithms)are already in place at airports in several countries,notably Schiphol Airport in The Netherlands,several Canadian airports, Frankfurt Airport,and at ten UK airport locations, starting inlate 2004.

The 420,000 registered UAE expellees representmore than 180 nationalities, and, of course, the2,128,300 arriving passengers who all have beencompared against that search database represent allnationalities. Since the majority of the world’speople have dark brown irises, which in the visiblewavelengths of light used (by definition) by ourhuman visual systems appear to show little irispattern structure or texture, it is often surprising tonew observers of this technology that irisrecognition can work at all with such populations,

let alone work so well. But in the infraredwavelengths of illumination (700 nm – 850 nm)used by iris cameras, such as the one pictured, evendark brown eyes usually reveal a rich texture morecomplex in detail than fingerprints.

Most nationals of the Arabian Gulf states havedark brown eyes, like the one shown in Figure 5illuminated in infrared light. But blue eyes are more

BIOMETRICS

Figure 3: An arriving passenger at Dubai Airportbeing compared against 420,000 registered irispatterns in about one second

Figure 4: An infrared wavelength iris camera, using thesame Daugman algorithms for iris recognition atSchiphol Airport, NL


common in the region than one might expect. Thisis deemed to be a legacy of the Crusades unleashedagainst the Middle East by medieval ChristianEurope. Like invading armies everywhere,Christendom’s Holy Crusaders left their geneticimprints behind in their wake.

How iris recognition worksAs described in an earlier general article(International Airport Review, Issue 3 2003), theiris recognition algorithms begin with locating theiris in an image, and determining the boundaries ofthe iris and the eyelids. The iris texture is thenencoded by a mathematical process calleddemodulation, creating a phase sequence thatresembles a DNA sequence in that it extracts localphase in four states. This representation of an irispattern is invariant to the size of the iris in theimage, and hence invariant to the distance of theeye and the optical magnification of the camera.It is also invariant to changes in the size of thepupil within the eye, because the polar coordinatesystem is dimensionless and automaticallycompensates for the elastic deformations in the irispattern as the pupil dilates or constricts.

Two examples of processed iris images, fromtwo different eyes, are illustrated. All colourinformation is discarded, so the infrared imagesappear black and white. The white outline graphicsshow results of the automatical localisationoperations, and the bit streams shown as 2D ‘barcodes’ are the actual IrisCodes, comprising 512bytes each.

The algorithm for encoding and comparing irispatterns is illustrated schematically in the networkdiagram. Iris patterns A and B are demodulatedinto phase sequences using mathematical functionscalled wavelets, producing a unique bit stream foreach iris. These IrisCodes are compared to eachother bit by bit (notionally one IrisCode for anarriving passenger is compared exhaustively againstevery IrisCode enrolled in the database), to see ifthey match. This question is decided simply by

counting the fraction of bits that disagreed betweentwo IrisCodes, which is a measure of dissimilaritycalled their Hamming Distance. Because thismeasure has an extremely well-defineddistribution, a simple threshold suffices to delivera ‘yes’ or ‘no’ answer.

Performance in testsThe distribution of Hamming Distances betweendifferent eyes’ iris patterns has a very stable andwell-defined mathematical form called a BinomialDistribution, as illustrated in Figure 8 in thedistribution plotted from 204 million suchcomparisons. The most important feature of thisdistribution is its very rapidly attenuating tails.These reduce to zero at an astronomical ratebecause the statistical probability of twoindependent bit sequences happening ‘just bychance’ to agree in significantly more or fewer than50% of their bits theoretically decays by factorial

functions, which are among the mostmathematically severe functions known. Thus, forexample, among the 204 million different eyecomparisons plotted in this distribution, no twoIrisCodes had a Hamming Distance smaller than0.290, which means that no two different eyes couldagree by chance in more than 71% of their bits(hence disagree in fewer than 29% of their bits).

BIOMETRICS

Figure 5: With infrared illumination, even dark browneyes reveal rich iris texture difficult to see in visiblewavelengths

Figure 6: Examples of the IrisCode bit streams computedfor two different eyes. The white outlines illustrateautomatic localisation of the iris and eyelid boundaries


This extraordinary property is the reason whyiris recognition has no rival among biometrics interms of accuracy and in reliability against making

False Matches. Physically, this is due to the amountof randomness contained in iris patterns asextracted by the algorithms, which can be calibratedmathematically in terms of the ‘number of degreesof freedom.’ Statistically, iris patterns (at least asdemodulated and encoded by the Daugmanalgorithms) contain about 250 degrees of freedom,which is far greater than that extracted by face

recognition or fingerprint algorithms from facesand fingerprints.

This basic mathematical advantage was reflectedin performance tests conducted in 2001 by the UKGovernment, comparing the accuracy of manydifferent biometric technologies. Their results weresummarised in the multi-curve plot showing thetrade-off between False Reject Rate and FalseAccept Rate for each biometric tested. Such curvesare called ROC (Receiver Operating Characteristic)curves, and they are generated by varying thedecision threshold to be more liberal or moreconservative, revealing a trade-off: the extent towhich any improvement in one of the two rates oferror must be ‘paid for’ by a worsening of the othererror rate. Clearly the goal is to get as far into thelower left-hand corner as possible, since thatcorresponds to achieving simultaneously theminimal number of False Accepts and False

Rejects. As the UK Government report (Figure 9)clearly shows, the Daugman iris recognitionalgorithms (represented by the isolated pointplotted in the lower-left corner) were without rivalamong all the biometrics tested. In 2.3 million iriscomparison tests, there were no False Matchesmade, and only 0.2% False Rejections on thethird attempt.

The UAE deployment of iris recognitiontechnology is currently the largest in the world,both in terms of number of persons enrolled(420,000) and number of iris comparisonsperformed daily (2.7 billion) in ‘all-against-all’search mode. An important aspect of its successfulperformance is the fact that the decision thresholdsare automatically adjusted according to the size

BIOMETRICS

Figure 7: How iris patterns are compared. Bit streamsthat encode the complex random iris textures of twoeyes, A and B, into phase sequences are fully comparedbit-by-bit. If the total fraction of disagreeing bits(Hamming Distance) is smaller than some threshold,adapted to database size, then the two iris patternsare deemed to be the same

Figure 8: Distribution of dissimilarity scores (HammingDistances) obtained from comparisons between 204million different iris pairings. Any given bit in twodifferent IrisCodes is equally like to agree or disagree,so the average score is near 0.5, and very few can befar from this score

Figure 9: Independent tests conducted by the UKGovernment in 2001, comparing the accuracy andreliability of many different biometric technologies. Byachieving simultaneously 0 False Matches and thelowest rate of False Rejects, the study showed that theDaugman algorithms described in this article (yellow)were without rival among biometrics


of the enrolled database, and hence the number ofcomparisons performed, so that there is no netaccumulation of probability of False Matchesdespite such a vast number of opportunities.Indeed, all of the 9,506 matches made so farbetween arriving passengers and former detaineesor expellees on the ‘watch list’ have beensubsequently confirmed by other records, exposingthe fraudulent travel documents.

Biometrics lacking the very favourable binomialdistribution tails discussed earlier cannot afford tooperate in this ‘all-against-all’ identification searchmode; they suffer from net accumulation of errorprobability and therefore make False Matches, justlike playing a game of Russian Roulette anincreasing number of times. When these favourablemathematical aspects of the iris recognitionalgorithms are combined with the sophisticateddatabase management, distributed hardware, andcommunications network of the IrisFarmarchitecture, as summarised in Figure 10, the resultis an extremely efficient and accurate system foridentification of persons on a national scale.Having proven itself decisively in the Emirates, thissystem is ready for service in diverse types of large-scale national deployments.■

BIOMETRICS

Figure 10: The distributed and fully networked ‘IrisFarm’ architecture developed by IrisGuard Inc. for the UAE bordercrossing and expellee tracking system. An unlimited number of parallel IrisEngines ensure scalability over nationaldatabases regardless of size, while synchronisation of data enrolled over diverse locations occurs without interruption ofsimultaneous real-time identification search requests

John Daugman, OBE, is a permanent member of theFaculty at Cambridge University, UK, where heteaches courses in Computer Vision, InformationTheory, and Pattern Recognition. He received his ABand PhD degrees at Harvard University, USA, where

he also subsequently taught on the Faculty. Daugman is the inventorof iris recognition for personal identification, for which he received USPatent 5,291,560 in 1994. These algorithms have won several awardsand medals from computer societies, and they are the softwarerunning in all current public deployments of iris recognition.For more information about this technology, its uses, and how itworks, visit http://www.CL.cam.ac.uk/users/jgd1000/

Imad Malhas is a founding member of IrisGuard Inc.and is the main designer of the IrisFarm Architecturefor real-time iris recognition, enrollment, anddatabase management on a national scale. Mr.Malhas has an extensive technical background in the

field of information technology and is considered a pioneer insoftware development in the Middle East. He received his B.Sc.degree in Computer Science from the University of Wisconsin(Milwaukee, USA) in 1984, thereafter holding various IT managerialpositions until founding IrisGuard Inc. in 2001. IrisGuard is today theworld leader in large-scale iris recognition systems deployment andintegration, and has offices in the UK and the Middle East. For moreinformation, visit http://www.irisguard.com/


Documents

Iris recognition border-crossing system in the UAEjgd1000/UAEdeployment.pdf · The 420,000 registered UAE expellees represent more than 180 nationalities, and, of course, the 2,128,300