Iris System

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Company

LOGO

I.R.I.SIRIS RECOGNITION & IDENTIFICATIONSYSTEM


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Presented byPresented by

Mr. Nilesh Purohit

Ms. Harshal Kalgutkar

(MASTER DEGREE IN INFORMATION TECHNOLOGY)

YEAR 2008-2009

THAKUR COLLEGE OF SCIENCE AND COMMERCEKANDIVALI (EAST), MUMBAI-400101

YEAR 2008-2009


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Nowadays, security systems take a very important place inpublic or private institutions which in

security systems,

visitor identification,

especially for building access control,

suspect identification by the police,

driver's licenses and many other fields.

In order to overcome the security problems biometricsystems can be used.

Iris recognition systems are the most accurate, reliable andefficient way to recognize and distinguish people.


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Introduction

BiometricsBIO + METRICS

The statistical measurement of biological data.

Biometric characteristics can be divided into twomain classes Physiological are related to shape of the body. Behavioral are related to behavior of the person.

A good biometric is characterized by use of afeature that is Highly unique Stable Easily captured


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Existing Systems

Physiological

Fingerprints.

Face recognition.

Hand geometry.

Behavioral

Signature.

Keystroke

Voice or speech


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Drawbacks of ExistingSystem

Fingerprint :Fingerprint readability also may be affected by the work anindividual does.

For example, transportation workers such as mechanics, foodworkers, may present fingerprints that are difficult to read due todryness or the presence of foreign substances, on fingers.

Facial Recognition:

Although facial recognition has success to verification, face

covering masks may change verification success rate.

People do not know always their pictures is being taken andsearched for database or picture may be taken without permissionof the user.


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Drawbacks of ExistingSystem

Hand Geometry:

Hand geometry not matches with large databases. Conditions suchas pregnancy or certain medications can affect hand size. Hand sizeand geometry changes during the life cycle of people.

Expensive equipments are required.


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Technology

The iris has been historically recognized to possess characteristicsunique to each individual. In the mid-1980s, two ophthalmologists

Dr. Leonard Flom

Aran Safirproposed the concept that no two irises are alike.

They researched and documented the potential of using the iris foridentifying people and were awarded a patent in 1987.

Soon after, sophisticated algorithm that brought the concept toreality was developed by Dr. John Daugman [1] and patented in

1994.

The technical performance capability of the iris recognitionalgorithm far surpasses that of any other biometric technology nowavailable.

They are at levels that cannot be reached by other biometrics.


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What is iris?

Structure of human eye


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What is iris?

The iris is the plainly visible, colored ring that surrounds the pupil.

It is a muscular structure that controls the amount of light enteringthe eye.

The iris is not to be confused with the retina, which lines the insideof the back of the eye.

I i i f t


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Iris posses unique feature

Pigmentation of the stroma takes place in the first fewyears. This depend on genetics.

Patterns of the iris is randomly formed, in depend togenetics.

No two irises are alike. It is epigenetic in nature.E.g.: identical twins posses uncorrelated iris patterns.

There is no detailed correlation between not only twins butalso in the right and left eye of an individual.

The amount of information that can be measured in a singleiris is much greater than fingerprints, and the accuracy isgreater than DNA.

Iris pattern remain almost stable throughout life


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Objective

Human identification using iris pattern has recently gainedgrowing interests from pattern researchers.

Aim is to create biometric system, where iris of human eyewill be used as unique feature of an individual forvalidating.


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Proposed system

General biometric system work as

P d t


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Proposed systemcontd..

Similarly iris recognition system will contain twosubsystem:

Template generator: This sub system will generate templateusing image processing technique.

Matcher: This sub system will compare between the templates.


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Scope

Image of a human eye will be captured using high-end camera (Infraredcamera).

From a digitalized image of human eye will be pre processed to extractunique iris pattern in form of iris code.

The feature extracted will be stored as hamming distance.

When subject wish to match with help of iris system, a hamming distancewill be calculated for their iris region.

The distance will be later compared with other hamming distance indatabase until match is found or subject is identified.


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Environment

Language used for coding Mat Lab 7.0 / Java/.NET

Operating system Windows XP

Hardware requirements 3.00 MHz Intel Pentium III processor( equivalent) and later512 MB120 GB available disk space

Other hardware requirements Infrared camera.


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S t a r t

I m a g e A c q u i s i t i o n

& P r e p r o c e s s i n g

I r is B o u n d a r y

D e f i n i t i o n

F i e l d

O p t im i z a t io n

I m a g e D a t a

A n a l y s i s

D i s t a n c e

C a lc u l a t io n

N e w E n t r y

H a m m i n g c o d es t o r e d i n t o t h e

d a t a b a s e

D a t a s t o r e d

M a t c h i n g w i t h

H a m m i n g c o d e in

t h e d a t a b a s e

R e s u l t

S t o p


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Process flow in detail

Image Acquisition & Preprocessing

Acquiring high quality image with infrared camera.

This will give us semi-processed image.

For further processing we use filters to remove noise.


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Iris Boundary Definition

The first stage of iris recognition is to isolate the actual irisregion in a digital eye image.

The iris region, can be approximated by two circles, one forthe iris/sclera boundary and another, interior to the first, forthe iris/pupil boundary.

Segmentation is used to do the same A technique is required to isolate and exclude these artifacts

as well as locating the circular iris region.

Hough Transform

Daugmans Integro-differential Operator

Active Contour Models Eyelash and Noise Detection


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The Hough transform is a standard computer vision algorithm thatcan be used to determine the parameters of simple geometricobjects, such as lines and circles, present in an image.

We are using circular Hough transform for detecting the iris andpupil boundaries. This involves first employing Canny edge

detection to generate an edge map that will help us to get the irisregion.

The circular Hough transform can be employed to deduce the radiusand centre coordinates of the pupil and iris regions.

Advantage of this is that we never a have complete circular. It isparabolic in shape which can be effectively processed by the abovetechnique.


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Normalization/ Field optimization: This is necessary as the pupil and iris dimension keep on

varying in real time. This is called dimensional inconsistencies. It is also caused due to varying imaging distance, rotation of the

camera, head tilt, and rotation of the eye within the eye socket.

Normalization produces images that have samedimension and characteristic feature at the same spatiallocation even when images are captured at differentcondition.


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Image Data Analysis Feature Encoding Techniques:

Wavelet Encoding

Gabor Filters

Log-Gabor Filters Zero-Crossings & 1D Wavelet

Haar Wavelet

Laplacian & Gaussian Filters


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Distance Calculation Techniques

Hamming Distance

Weighted Euclidean Distance

Normalized Correlation

We will use Hamming distance.

Using the Hamming distance of two bit patterns, a decision can be madeas to whether the two patterns were generated from different irises orfrom the same one.


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Matching: For matching, we will use the Hamming distance as a metric for

recognition, since bit-wise comparisons were necessary. The Hammingdistance algorithm employed also incorporates noise masking, so thatonly significant bits are used in calculating the Hamming distancebetween two iris templates.

After the matching process we get the result whether the pattern is ofsame iris or is a new type.


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The EndThank You

Documents

Iris System