Jenny Chan 01062007 Handout

7/29/2019 Jenny Chan 01062007 Handout

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Biometric Technology---Iris Recognition

Presented by Mei-Jane Chan (Jenny)


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How Iris Recognition works?

Source: http://www.gii.upv.es/personal/gbenet/treballs


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Covered Topics What is Biometric Identification Systems?

The Unique characteristics of Iris---Why Iris Recognition is better than other

biometrics? How Iris Recognition works?

SWOT Analysis

Conclusion


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What Is Biometrics?Technology for automated recognition or verificationof the identity of a person using unique physical or

behavioral characteristics such as fingerprints, handgeometry, iris, voice, and signatures.

Establishes an identitybased on what you are,-- Biometricsrather than

what you possess

what you remember

traditional


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Behavioral BiometricsSpeaker Recognition

Signature

Keystrokewalking style

voice recognition

*Change according to

al conditionpsychologic*Influenced by physical traits

(men/women, build)*Change a lotSimple, inexpensive equipmentNon-.offensive method

Physical BiometricsFingerprint

Facial Recognition

Hand GeometryIris Scan

Retinal Scan

DNA

Bertillonage

Vascular Patterns

*Relatively stable*Does not change much in a

lifetime*Huge, expensive equipmentneeded.*Intrusive method

Current identity systems make use of

biometrics :

Source:

http://www.idteck.com/technology/biometrics.jsp


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Why Biometrics? Security and Management

Convenience Easier fraud detection

Better than password/PIN or smartcards

No need to memorize passwords

Requires physical presence of the person to

be identified Unique physical or behavioral characteristic

Cannot be borrowed, stolen, or forgotten

Cannot leave it at home


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Biometrics Systems Overview

Enrollment Capturing biometric trait using sensor device Extracting relevant features to generate template Store template in database

Verification Generate template as in enrollment Match the template against a specific template

one-to-one search (1:1) Used for physical or computer access

Identification Match done against a set oftemplates

one-to-many search (1:N)

Used in identifying criminals


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Basic Process Flow: Biometric

System Architecture

Identification/Verification Phase

Enrollment PhaseData Capture &Conditioning

FeatureExtraction

TemplateFormation

TemplateDatabase

Data Capture &

Conditioning

Feature

Extraction

Template

Formation

TemplateMatcher

Decision

Output



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Ideal Traits Needed By Biometric

Systems

Universal

everyone has it

Unique

no two people have it alike Permanent

does not change and cannot be changed

Collectable

easy to obtain and quantify with a sensor


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Comparison of Major Biometric Techniques


LowLowHighLowHighHighHighDNA

LowMediumMediumMediumMediumMediumMediumVein

HighHighLowMediumLowLowMediumVoice

HighHighLowHighLowLowLowSignature

LowLowHighMediumHighHighHighIris

LowMediumHighMediumHighHighMediumFingerprint

LowLowLowHighMediumLowHighFace

Potential

to

fraud

Accepta-

bility

Perfor-

manc

e

Collect

able

Perman

enceUniqueUniversalBiometrics


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Iris RecognitionIris Recognition IntroductionIntroduction Iris recognition analyzes the features that exist in

the colored tissue surrounding the pupil, which has250 points used for comparison, including rings,

furrows, and freckles.

Iris recognition uses a regular video camerasystem and can be done from further away than a

retinal scan.

It has the ability to create an accurate enoughmeasurement that can be used for Identification

purposes, not just verification.


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Iris RecognitionIris Recognition IntroductionIntroduction

The probability of finding two people with identicaliris patterns is considered to be approximately 1 in1052 (population of the earth is of the order 1010).Not even one-egged twins or a future clone of aperson will have the same iris patterns.

The iris is considered to be an internal organbecause it is so well protected by the eyelid andthe cornea from environmental damage.

It is stable over time even though the personages.

Iris recognition is the most precise and fastest ofthe biometric authentication methods


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Why Iris Recognition is better than other biometrics

Features of the iris

Measurable Physical Features: 250 degrees offreedom,250 non-related unique features of a persons iris

Unique: Every iris is absolutely unique. No two iris

are the sameStable: iris remains stable from 1st year till death.

Accurate :Iris recognition is the most accurate of

the commonly used biometric technologies.Fast :Iris recognition takes less than 2 seconds.20 times more matches per minute than its closestcompetitor.

Non-Invasive :No bright lights or lasers are usedin the imaging and iris authentication process.


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Iris Recognition-Accuracy

Telephone

service

Low1/30Voice characteristicsVoiceprinting

Low-security

facilities

Low1/100Shape of letters, writing

order, pen pressure

Signature

Low-security

facilities

Low1/100Outline, shape and

distribution of eyes and

nose

Facial

recognition

Low-security

facilities

Low1/700Size, length, and thickness of

hands

Hand shape

UniversalMedium1/1,000FingerprintsFingerprinting

High-security

facilities

High1/1,200,000Iris PatternIris

recognition

ApplicationsSecurityMisidentification

rate

Coded patternMethod

source: AIM Japan, Automatic Identification seminar,

Sep 14,2001


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SPEED PERFORMANCE---A iris recognition system representated on a PIII-550 Mhz

workstation

100455 msTotal time

< 0.2< 1 msIris code matching

< 0.2< 1 msIris code extraction

41187 ms2D Freq. demodulation

3.516 msPolar reference

55250 msIris localization

% of total timeTime consuming

source : http://www.gii.upv.es/personal/gbenet/treballs


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3. How3. How Iris RecognitionIris Recognition worksworks

The process of iris recognitionStep1:

Iris image acquisition by using a monochrome CCD

(Charged couple Device) camera.Step2:

Preprocessing of the image by locating the iris,

normalizing the iris and enhancing the image.

Step3:

Extracting the local features of the iris.

Step4:

Matching the iris-code with an already stored iris code


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Access to restricted areas at US airports

http://www.cl.cam.ac.uk/~jgd1000/SchipholEnrollment.ht

ml


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Recognition of frequent flyers in lieu of passport presentation

http://www.cl.cam.ac.uk/~jgd1000/SchipholEnrollment.html


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Enrollment of frequent flyers at Schiphol Airport, NL.



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Condominium entry control (with automatic elevator calling

and programming).



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In lieu of passport presentation at Frankfurt/Main Airport



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WatchList screening of all arriving foreigners at UAE ports of entry



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Expedited check-in of departing passengers at Narita Airport


Iris RecognitionIris Recognition 11 Image acquisitionImage acquisition


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Iris RecognitionIris Recognition 1.1.Image acquisitionImage acquisition



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Finding an Iris in an image by a monochrome CCD(Charged couple Device) camera

Transfer the value of the different photosites outof the CCD chip.

Reading out the voltages from the CCD-chip

Thereafter the signals of each Data are amplified

and sent to an ADC (Analog to Digital Converter)

http://www.cl.cam.ac.uk/~jgd1000/


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Iris RecognitionIris Recognition1.1. Image acquisitionImage acquisition

The image acquisition is done by a

monochrome CCD-camera (640x480)covering the iris radius with at least 70photosites(=pixels).

The camera is situated normally between halfa meter to one meter from the subject.(3 to

10 inches) It uses near infrared(NIR) illumination ,so

darker irises will show more details.

Iris RecognitionIris Recognition 11 Image acquisitionImage acquisition


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The CCD-camera The CCD-cameras

job is to take theimage from theoptical system and

convert it intoelectronic data:

Source: www.cl.cam.ac.uk/~jgd1000/

Iris RecognitionIris Recognition 22 PreprocessingPreprocessing


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Iris RecognitionIris Recognition 2.2.PreprocessingPreprocessing

Iris localization:

* The task consists of localizing the inner and outerboundaries of the iris. Both are circular, but theproblem lies in the fact that they are not co-centric The two circles must be calculated seperately.

*To do this a circle detection variant of the normallyline detecting Hough-transformation is applied byUsing polar coordinate system

Source :http://www.cl.cam.ac.uk/~jgd1000/

Iris RecognitionIris Recognition 22. PreprocessingPreprocessing


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Iris RecognitionIris Recognition 2.2. PreprocessingPreprocessing

Normalization: Two images of the same iris might be very different as a result of:

The size of the image.

Size of the pupil. Orientation of the iris.

To cope with this, the image is normalized by converting to doublydimensionless polar

Pseudo polar coordinate system

-it ranges from the pupillary boundary to limbus always as a unit

interval 0 to 1



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Enhancement: It is necessary to enhance the image to be able to

extract the iris patterns later.

The enhancement consist of Sharpening the picture with a sharpening mask

Reducing the effect of non-uniform illumination with

local histogram equalization

Figure 1 : Texture image before enhancement.

Figure 2 : Texture image after enhancement.

http://www.rpi.edu/AFS/home/03/kimjw/public_html/


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Iris RecognitionIris Recognition 3.3.Iris code constructionIris code construction

To construct the iris code,the patterns are encoded using

2D Gabor wavelet demodulation(512byte) to collect the iriscode with a length of 256 bytes (2048 bits).

This is done by using various features of the iris to come up

with a basic code a highly complex mathematical processfor computation which developed and patened by JohnDaugman ( Cambridge University )

Once the code has been computed, statistical tests can berun on it to compare it with other iris codes and search for amatch.


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The 2-D Gabor wavelet demodulation used for iris recognition are

defined in the doubly dimensionless polar coordinate

system (r, ) as shown in figure below:


Iris RecognitionIris Recognition 3.3. the test of statistical Independence :the test of statistical Independence :C f


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Combinatorics of phase sequencesCombinatorics of phase sequences

Hamming Distance Calculation

The difference between iris codes are measured by theHamming Distance (HD), which is the number of

disagreeing bits between two iris-codes. Using XOR operatordetects disagreement between

any corresponding pair of bits Hamming Distance

Comparison of Iriscode records ,which is a measure ofvariation between the Iriscode record from the presentediris and each Iriscode record in the database.

There have been identified about 250 degrees of freedomin the iris


Iris Recognition ---Iris Verification


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Ex :Measure of variation between a live iris and each

template stored in the database Hamming Distance CalculationHamming Distance Calculation


33 Iris RecognitionIris Recognition Iris code constructionIris code construction


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3.3.Iris RecognitionIris Recognition -- Iris code constructionIris code construction

Verification and identification : Boolean XOR operatordetects disagreement between

any corresponding pair of bits Hamming Distance

codeA: captured iris code ex) 1101011

codeB: Stored iris code ex) 1000011maskA: Mask bit vector of captured iris codeex) 1001111maskB: Mask bit vector of stored iris codeex) 1100111

||||

||)(||

maskBmaskA

maskBmaskAcodeBcodeAHD

=

Source :


Iris RecognitionIris Recognition Recognizing irises regardless of size


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Iris RecognitionIris Recognition Recognizing irises regardless of size,position, and orientation

Images are captured in non-ideal conditions for HDdistribution for same eyes


Iris RecognitionIris Recognition Iris code construction andIris code construction andEntropy MeasuresEntropy Measures


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Images are captured in ideal conditions

Entropy MeasuresEntropy Measures

Source : http://www.rpi.edu/AFS/home/03/kimjw/public_html/

St ti ti l d t f h i di t


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Statistical data of hamming distance

Arbitrary test iris codes and registered iris codehas hamming distance of mean value around 0.5

http://www.cl.cam.ac.uk/~jgd1000/http://www.rpi.edu/AFS/home/03/kimjw/public_html/
http://www.cl.cam.ac.uk/~jgd1000/http://www.cl.cam.ac.uk/~jgd1000/http://www.cl.cam.ac.uk/~jgd1000/


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Uniqueness of failing the test of statisticaltest of statistical

IndependenceIndependence in 9.1 million pairs comparison

All testing organizations have reported a falsematch rate of 0 in their tests.

British Telecom US Sandia Labs UK National Physical lab, NBTC Panasonic, LG Oki EyeTicket IBM


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John Daugman (Iriscode Designer) John Daugman is a physicist and

computer-vision expert at the University

of Cambridge Computer Laboratory. He is best known for his pioneering

work in biometric identification, in

particular the development of theIrisCode algorithm that is (as of 2006)the basis of all commercially availablebiometric iris recognition systems

In 1994, Dr. Daugman patented thealgorithms, which are owned by IridianTechnologies, Inc.

http://www.cl.cam.ac.uk/users/jgd1000/history.

html


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SWOT Analysis -StrengthsHighly Protected:

internal organ of the eyeExternally visible; patterns imaged from a distance

Measurable Features of iris patternIris patterns possess a high degree of randomnessvariability: 244 degrees-of-freedomentropy: 3.2 bits per square-millimeter

Uniqueness:set by combinatorial complexity

Stable :Patterns apparently stable throughout life

Quick and accurate--- Encoding and decision-making are tractableimage analysis and encoding time: 1 second

search speed: 100,000 IrisCodes per second on 300MHz CPU

SWOT Analysis - Weaknesses


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y

Some difficulty in usage as individuals dont

know exactly where they should positionthemselves.

and if the person to be identified is notcooperating by holding the head still andlooking into the camera.

Acquisition device is much more expensive

Analysis SWOT-Strengths and


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y g

Weaknesses

Threat

Cost High-end physical security unit : $4000~5000 range Home or office : $700~800 range. Other competitors

Opportunity

More and more needed for Automatic Identification Systems.

Better solution for increased security requirements than traditionalmethods

Iris identification has low error rate Hopes to break the $500 price barrier.


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Impact of Iris Recognition

An award winning access control system Uses identification, (one to many) not verification (one to one)

matching It is non-contact. Works with glasses, protective clothing, safety

shields and contact lenses Images the iris which is stable over life. One enrollment only Is non-invasive .Uses Video based technology Has extremely fast database matching (match rates in excess of

100,000 /per second achieved on a standard PC)


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Conclusion The iris technology combines computer vision, pattern

recognition , statistical inference, and optics. Its purpose

is real-time, high confidence recognition of a personsidentity by mathematical analysis of random patterns thatare visible within iris of an eye from some distance.

The iris technology is expanding into the most reliablebiometrics feature -- -data rich of physical structure,accurate, secure, stable, and safe

The only disadvantage was the designing andmanufacturing cost

Reference :


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Reference :

Recognizing Persons By Their Iris - Dr Daugman

http://www.cl.cam.ac.uk/users/jgd1000/history.html

Technical Paper: How Iris Recognition Works - DrDaugman

http://www.cl.cam.ac.uk/users/jgd1000/history.html

Iris Recognition Technologyhttp://www.argus-solutions.com/pdfs/irisrecogwilliams.pdf


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Thank you very much

Q & A


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Movie poster for the Minority Report(2002)filmSource : http://en.wikipedia.org/wiki/Image:Minority_Report.jpg
http://en.wikipedia.org/wiki/Minority_Report_%28film%29http://en.wikipedia.org/wiki/Minority_Report_%28film%29

Jenny Chan 01062007 Handout

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