Chapter 1

Introduction

In recent past, terror of several terrorist groups and act of outlaw is been spreading rapidly in

India. Tracking those acts before it is caused or recovering from it has become difficult to our

security system. That failure is due to lack of integrated information from different department t

of India like: passport, transport, police, defense and, income tax etc. Moreover, tracking in-

depth information about ever individual is effected due to over population of our country.

In order to overcome the security problems a systems which has integrated information of all

department of India along with some Biometric system to uniquely identify ever individual. A

biometric system provides automatic recognition of an individual based on some sort of unique

feature or characteristic possessed by the individual.

Biometric systems work by first capturing a sample of the feature, such as recording a digital

sound signal for voice recognition, or taking a digital color image for face recognition. The

sample is then transformed using some sort of mathematical function into a biometric template.

The biometric template will provide a normalized, efficient and highly discriminating

representation of the feature, which can then be objectively compared with other templates in

order to determine identity. Most biometric systems allow two modes of operation. An enrolment

mode for adding templates to a database, and an identification mode, where a template is created

for an individual and then a match is searched for in the database of pre-enrolled templates.

1.1 OBJECTIVE

The Development & Implementation of this Iris Based Security System with Iris recognition is

aiming to:

Develop an integrated Iris based security system containing detailed information of

citizen in the country.

Iris based security system should be enabled with biometric system.

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Biometric system, where iris of human eye will be used as unique feature of a citizen for

validating.

To provide fast, easy and integrated information of a citizen.

To reduce the time factor needed for searching information of a citizen form different

department of Indian security.

1.2 PROBLEM STATEMENT

Conventional systems cannot check passwords, PIN chip and identity cards to see if the

user providing correct data is also the lawful owner. As biometric techniques work with

person-linked characteristics (which can neither be lost nor forgotten, and are not easy to

steal), they promise a new dimension in quality, comfort and security in personal

authentication.

But many of the biometric authentication systems are overshadowed by the very high

failure rate. Recognition systems, such as finger-print recognition, voice recognition, etc.

are usually impeded by external factors i.e. Finger print can be damaged by scarring

and voice may change with age. Another occular based recognition system, retinal scan,

even though highly accurate are not very user friendly as they are intrusive. Hence, these

systems tend to false reject or are unfriendly to user.

Iris recognition on the other hand is rarely affected by external factors as it is protected by

external transparent sclera and eyelids and is stable i.e. A single enrollment in the database

can last a lifetime. Unlike retinal scan iris image can be captured us ing a cam- era

illuminated by infrared light from a distance. Even though, iris recognition is relatively

young biometric authentication system, it is one of the fastest emerging recognition systems.

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1.3 SCOPE

The system will have a console system implementation and three-tier architecture.

The middle layer will contain logics of iris pattern generator and matcher and the data would be

stored in external data stores which the user that is admin can access from graphical user

interface

Fig.1.3.1: Three tier architecture of Iris based security system

Admin can login to the system, and do various processes.

Admin can add or edit person information.

Admin can when search a person on the system by providing necessary credential which

can be printed also.

Admin can add organization detail.

Admin can add institutional detail.

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Study of Iris

The iris is located behind the transparent cornea and aqueous humour of the eye, but in

front of the lens. It is a membrane in the eye, responsible for controlling the diameter and

size of the central darker pupil and the amount of light reaching the retina. It is a colored

ring around the pupil. The color of the iris is the “Eye Color”, which can be green, blue, or

brown. Its only physiological purpose is of course to control the amount of light that

enters the eye through the pupil, by the action of its dilator and sphynctor muscles that

control pupil size, but its construction f r o m elastic connective tissue gives it a complex,

fibrillose pattern. The larger the pupil, the more light can enter.

Fig 1.3.2: Schematic Diagram of the Human Eye

Structure of the iris

Fig 1.3.2: Cross Section of iris

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The iris is divided into two major regions:

1. The pupillary zone is the inner region whose edge forms the boundary of the pupil.

2. The ciliary zone is the rest of the iris that extends to its origin at the ciliary body.

Iris surface features

1. The pupillary ruff is a series of small ridges at the pupillary margin formed by the

continuation of the pigmented epithelium from the posterior surface.

2. The Circular contraction folds, also known as contraction furrows, are a series of

circular bands or folds about midway between the collaret and the origin of the iris. These

folds result from changes in the surface of the iris as it dilates.

3. Crypts at the base of the iris are additional openings that can be observed close to the

outermost part of the ciliary portion of the iris.

Features of Iris

The iris has many features that can be used to distinguish one iris from another. One of the

primary visible characteristics is the trabecular meshwork, a tissue which gives the

appearance of dividing the iris in radial fashion that is permanently formed by the eighth

month of gestation. During the development of the iris, there is no genetic influence on it,

a process known as chaotic morphogenesis that occurs during the seventh month of

gestation, which means that even identical twins have different irises.

The fact that the iris is protected behind the eyelid, cornea, and aqueous humor means

that, unlike other biometrics such as fingerprints, the likelihood of damage or abrasion is

minimal. The iris is also not subject to the effects of aging which means it remains in a

stable form from about the age of one until death. The use of glasses or contact l enses

(colored or clear) has little effect on the representation of the iris and hence does not

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interfere with the recognition technology.

Fig 1.3.3: A front-on view of the human eye

Since the iris in this sense is highly unique, stable and easily captured, it is complex

enough to be used as a biometric signature.

1.4 BIOMETRICS

Biometric is the process of uniquely identifying humans based on their physical or be-

havioral traits. Biometric systems are mainly based on fingerprints, facial features, voice,

hand geometry, handwriting, and the one presented in our project, the iris. Biometric sys-

tems first captures a sample of the feature. This extracted feature is then transformed into

mathematical function or biometric template. The biometric template i s a normalized,

efficient and highly discriminating representation of the feature, which can then be objec-

tively compared with other templates in order to determine identity.

Biometric characteristics can be divided into two main classes

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Physiological are related to shape of the body.

Like Fingerprints, Face recognition, Hand geometry etc.

Behavioral are related to behavior of the person.

Like Signature, Keystroke, Voice or speech

A good biometric template is characterized by the use of a feature that i s :-

1. Highly Unique:

A biometric template shou ld be unique. This allows a person to be uniquely iden-

tified with minimum failures.

2. Stable:

Biometric feature of the person should remain same or change very little so that the

enrolled template in database will still be matchable. The likelihood of damage and

abrasion to this feature should be minimal.

3. Easily captured:

Biometric system should be able to extract t h e biometric feature effectively and

efficiently. Hence, the features should be externally visible.

 

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Fig 1.4.1: General Block Diagram of a Biometric System

1.5 IRIS RECOGNITION

Iris recognition is a method of biometric authentication that recognizes a person by pattern

of the iris. Iris recognition should not be confused with another ocular-based technology,

retina scanning. The automated method of iris recognition is relatively young, existing in

patent only since 1994 and is still considered the most accurate.

It uses a specialized camera technology, with subtle infrared illumination reducing specular

reflection from the convex cornea, to create images of the detail-rich, intricate structures

of the iris. Converted into digital templates, t h e s e images provide mathematical

representations of the iris that y ie ld unambiguous positive identification of an individual.

Iris recognition technology offers the highest accuracy in identifying individuals of any

method available. This is because no two irises are alike - not between identical twins, or

even between the left and right eye of the same person. Irises are also stable; unlike other

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identifying characteristics that can change with age; the pattern of one’s iris is fully formed

by ten months of age and remains the same for the duration of their lifetime. Iris

recognition is rarely impeded by glasses or contact lenses and can be scanned from 10cm to

a few meters away.

Gathering unique information of an individual from iris pattern requires extracting t h i s

pattern and encoding it into a bit-wise biometric template. Therefore, iris recognition

algorithms need to isolate and exclude the artifacts as well as locate the circular iris

region from the acquired eye image. Artifacts in iris include eyelids and eyelashes partially

covering it.

Then, the extracted i r i s region needs to be normalized. The normalization p rocess will

produce iris regions, which have the same constant dimensions, so that two photographs of

the same iris under different conditions will have characteristic features at the same spatial

location i.e. It involves unwrapping the doughnut shaped extracted i r i s e s into a constant

dimensional rectangle.

The significant features of the normalized iris must be encoded so that c o m p a r i s o n s

between templates can be made. Most iris recognition algorithms make use of a band pass

decomposition of the iris image to create a biometric template. Finally, templates a r e

matched using Hamming distance. The Hamming distance gives a measure of how many

bits are same between two bit patterns. Using the Hamming distance of two bit patterns, a

decision can be made as to whether the two patterns were generated from different irises or

from the same one.

Process of IRIS Recognition System can be summarized as follows:-

Fig 1.5.1: Block Diagram of Iris Recognition System

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Therefore, the iris is an externally visible, yet protected organ whose unique pattern re-

mains stable throughout the human life. These characteristics make it one of the most

preferred biometric techniques for identifying individuals. Digital image processing tech-

niques can be employed to extract t h e unique iris pattern from a digitized image of the

eye, and encode it into a biometric template. This biometric template contains an

objective mathematical representation of the unique information stored in the iris, and

allows comparisons to be made between templates.

Chapter 2

Literature Survey

The iris has been historically recognized to possess characteristics unique to each

individual. In the mid-1980s, two ophthalmologists—

Dr. Leonard Flom

Aran Safir

Proposed the concept that no two irises are alike.

They researched and documented the potential of using the iris for identifying people and

were awarded a patent in 1987.

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Soon after, sophisticated algorithm that brought the concept to reality was developed by

Dr. John Daugman and patented in 1994.

Since then many other systems have been developed, most notable include the systems of

Wildes, Boles and Boashash, Lim and Noh.

2.1 EXISTING SYSTEM

Existing systems are as follow:

The existing systems of security practiced in India are usually primitive.

Fig 2.1: Birth Certificate

A citizen is identified on the bases of paper documents like birth certificate, identification

card, pan card or identification parade.

Fig 2.1: PAN Card

While some security areas are automated with motion detector alarm system, barcode

system, smart card etc...

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Fig 2.1: Smart card

Fig 2.1: Barcode ID

Biometric systems are also used for unique identification of citizen like fingerprint

recognition, face recognition, DNA testing.

Fig 2.1: Fingerprint recognition

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Data of all departments of security like defense, police or C.B.I is not integrated.

2.1.1 DRAWBACKS OF EXISTING SYSTEM

The following are some of the drawbacks that were observed in the existing system.

High maintenance

o Identification documents like birth certificate, PAN card, smart card or

barcode ID, it can worn out with the time or spoil due to in contact with

some foreign element like water. So need to be maintained. Moreover

identification document need to be carried along always.

Time taken

o Take taken for identification with identification parade is long. Biometric

identification of DNA testing is long procedure and takes lot of time.

High chance of fraudulence

o There are high chances of fraudulence of identification document

including smart card and barcode ID, which can cheat the security system.

Faulty result

o Identification parade and other identification document can also led to

faulty result in some cases. As wrong person is identified in identification

parade. Moreover, in biometric system like finger print can be identical in

case of twins.

Affected by the nature of work

o Fingerprint readability also may be affected by the work an individual

does. For example, transportation workers such as mechanics, food

workers, may present fingerprints that are difficult to read due to dryness

or the presence of foreign substances, on fingers.

Distributed Information

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o Information of individual is distributed in different department of India

which make tracking of all information difficult in short time.

2.2 PROPOSED SYSTEM

The system at hand can be divided into two inter-related subsystems:

Iris pattern generator and matcher system:

This part of the system deals generating and compare iris pattern using various image processing

technique.

There are various processes that are handled by this system are:

1. Unique feature extracted of iris.

2. Iris pattern is generated and stored into the database.

3. If required Iris pattern is matched to the iris patterns stored in the database

Fig 2.2: Block Diagram of Iris Based Security System

Management system :

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This part deals with the management side of the security system. The various

processes handled by this system are:

The management can register and edit details of a human like personal information,

educational etc.

The management can search and retrieve the information about any human.

Management can register new educational and organization names with there unique

code.

ADVANTAGES OF PROPOSED SYSTEM

Attractive and user friendly graphical user interface.

Unique bio-metric feature used for identifying the citizen.

It provides help which make the system user, easy to understand the use of the system.

It is an integrated Iris based security system containing detailed information of citizen in

the country.

Iris based security system should be enabled with biometric system, where iris of human

eye will be used as unique feature of a citizen for validating.

It is advantageous due to the fact that iris is one human body part, which is less prone to

damage, as compared to fingers, hands and other parts used for physical biometrics.

It provides fast, easy and integrated information of a citizen.

It reduces the time factor needed for searching information of a citizen form different

department of Indian security.

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Chapter 3

Analysis

System has been implemented in this environment:

Operating System Windows XP Professional with SP3

Environment MATLAB®

Application

Development

Presentation Tier

UI Programming using Matlab, flash & Photoshop

Middle Tier

Logic Development matlab language

Data Tier

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Relational Tables in MS Access

RDBMS Micro soft Access

CPU Intel Pentium

Intel Celeron

AMD Athlon or AMD Duron Processor

Running at 300 MHz or faster

RAM 512 MB

Hardware

requirements

3.00 MHz Intel Pentium III processor ( equivalent) and later

120 GB available disk space.

Feasibility study

When developing software, the highlighted concept is mainly the feasibility of the system i.e.

whether the system is feasible in the following contexts:

Technical feasibility

Technical aspects were considered while the feasibility study was conducted. Since the nation

has licensed copy of all the software required for the system as well as necessary hardware to

meet the requirement of Infrared camera of the new Iris based security system, it can be

concluded that the system is technically feasible.

Operational feasibility

New employees are required to administer and maintain, for the same small training program is

required.

This training will include briefing of the Iris based security system with case and role-plays.

Initially a demonstration will be allowed to use the Iris based security system in presence of

developers. Any problems encountered can thus be taken care of. As the users have originated

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the request for a new system, acceptability is expected to be high. Hence we can conclude that

the Iris based security system is operationally feasible.

Financial feasibility

A financial feasibility study was carried out to know the financial viability of the project in the

terms of the amount of investment in the project and output expected.

The study includes the cost involved at the time of development of the project as well as the

future cost in terms of maintenance of software and other miscellaneous expenditure.

The proposed hardware and software are affordable cost. Cost of developing the software is very

little. On the basis of analysis the study concluded that the project is financial viable.

Start-up cost

1. Salaries of programmers

2. Cost of training

3. Preparation of manuals and other documents

Operating cost

1. Salaries of administrator & management people.

2. Cost of installation and maintenance.

3. Cost of high end Infrared camera.

Result of feasibility study

On all the three levels i.e. Technical, operational and economical level, the proposed system is feasible. 

Iris based security system is providing more benefits when compared with the Traditional practice 

mainly on the basis of costs in terms of time & overhead incurred.

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Chapter 4

Methodology

4.1 IMAGE ACQUISITION

This step is one of the most important and deciding factors for obtaining a good result. A good

and clear image eliminates the process of noise removal and also helps in avoiding errors in

calculation. In this case, computational errors are avoided due to absence of reflections, and

because the images have been taken from close proximity. This project uses the image provided

by CASIA (Institute of Automation, Chinese Academy of Sciences) . These images were taken

solely for the purpose of iris recognition software research and implementation. Infra-red light

was used for illuminating the eye, and hence they do not involve any specular reflections. Some

part of the computation which involves removal of errors due to reflections in the image were

hence not implemented.

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4.2 EYE IMAGE SEGMENTATION

Segment Eye performs automatic segmentation of the iris region from an eye image. Also

isolates noise areas such as occluding eyelids and eyelashes.

Here we will find:

circleiris - centre coordinates and radius of the detected iris boundary

circlepupil - centre coordinates and radius of the detected pupil boundary

imagewithnoise - original eye image, but with location of noise marked with

NaN values

Then we find the Top Eye Lid & Bottom EyeLid.

4.3 IRIS LOCALIZATION

To find the main IRIS boundary we will be using the CANNY EDGE detection & HOUGH

transform.

4.3.1 EDGE DETECTION

Edges characterize boundaries and are therefore a problem of fundamental importance in image

processing. Edges in images are areas with strong intensity contrasts – a jump in intensity from

one pixel to the next. Edge detecting an image significantly reduces the amount of data and

filters out useless information, while preserving the important structural properties in an

image. This was also stated in my Sobel and Laplace edge detection tutorial, but I just wanted

reemphasize the point of why you would want to detect edges.

The Canny edge detection algorithm is known to many as the optimal edge detector. Canny's

intentions were to enhance the many edge detectors already out at the time he started his work.

He was very successful in achieving his goal and his ideas and methods can be found in his

paper, "A Computational Approach to Edge Detection". In his paper, he followed a list of criteria

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to improve current methods of edge detection. The first and most obvious is low error rate. It is

important that edges occurring in images should not be missed and that there be NO responses to

non-edges. The second criterion is that the edge points be well localized. In other words, the

distance between the edge pixels as found by the detector and the actual edge is to be at a

minimum. A third criterion is to have only one response to a single edge. This was implemented

because the first 2 were not substantial enough to completely eliminate the possibility of multiple

responses to an edge.

Based on these criteria, the canny edge detector first smoothes the image to eliminate and noise.

It then finds the image gradient to highlight regions with high spatial derivatives. The algorithm

then tracks along these regions and suppresses any pixel that is not at the maximum (no

maximum suppression). The gradient array is now further reduced by hysteresis.Hysteresis is

used to track along the remaining pixels that have not been suppressed.

Hysteresis uses two thresholds and if the magnitude is below the first threshold, it is set to zero

(made a nonedge). If the magnitude is above the high threshold, it is made an edge. And if the

magnitude is between the 2 thresholds, then it is set to zero unless there is a path from this pixel

to a pixel with a gradient above T2.

Canny Edge includes: GAUSSIAN LOWPASS FILTER

The image was filtered using Gaussian filter, which blurs the image and reduces effects due to noise. The

degree of smoothening is decided by the standard deviation, σ and it is taken to be 2 in this case.

Example:

Step1

In order to implement the canny edge detector algorithm, a series of steps must be followed. The

first step is to filter out any noise in the original image before trying to locate and detect any

edges. And because the Gaussian filter can be computed using a simple mask, it is used

exclusively in the Canny algorithm. Once a suitable mask has been calculated, the Gaussian

smoothing can be performed using standard convolution methods. A convolution mask is usually

much smaller than the actual image. As a result, the mask is slid over the image, manipulating a

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square of pixels at a time. The larger the width of the Gaussian mask, the lower is the

detector's sensitivity to noise. The localization error in the detected edges also increases slightly

as the Gaussian width is increased. The Gaussian mask used in my implementation is shown

below.

Step2

After smoothing the image and eliminating the noise, the next step is to find the edge strength by

taking the gradient of the image. The Sobel operator performs a 2-D spatial gradient

measurement on an image. Then, the approximate absolute gradient magnitude (edge strength) at

each point can be found. The Sobel operator uses a pair of 3x3 convolution masks, one

estimating the gradient in the x-direction (columns) and the other estimating the gradient in the

y-direction (rows). They are shown below:

The magnitude, or EDGE STRENGTH, of the gradient is then approximated using the formula:

|G| = |Gx| + |Gy| 22

Step3

Finding the edge direction is trivial once the gradient in the x and y directions are known.

However, you will generate an error whenever sumX is equal to zero. So in the code there has to

be a restriction set whenever this takes place. Whenever the gradient in the x direction is equal to

zero, the edge direction has to be equal to 90 degrees or 0 degrees, depending on what the value

of the gradient in the y-direction is equal to. If GY has a value of zero, the edge direction will

equal 0 degrees. Otherwise the edge direction will equal 90 degrees. The formula for finding the

edge direction is just:

theta = invtan (Gy / Gx)

Step4

Once the edge direction is known, the next step is to relate the edge direction to a direction that

can be traced in an image. So if the pixels of a 5x5 image are aligned as follows:

x     x     x     x     x

x     x     x     x     x

x     x     a     x     x

x     x     x     x     x

x     x     x     x     x

Then, it can be seen by looking at pixel "a", there are only four possible directions when

describing the surrounding pixels - 0 degrees (in the horizontal direction), 45 degrees (along the

positive diagonal), 90 degrees (in the vertical direction), or 135 degrees (along the negative

diagonal). So now the edge orientation has to be resolved into one of these four directions

depending on which direction it is closest to (e.g. if the orientation angle is found to be 3

degrees, make it zero degrees). Think of this as taking a semicircle and dividing it into 5 regions.

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Therefore, any edge direction falling within the yellow range (0 to 22.5 & 157.5 to 180 degrees)

is set to 0 degrees. Any edge direction falling in the green range (22.5 to 67.5 degrees) is set to

45 degrees. Any edge direction falling in the blue range (67.5 to 112.5 degrees) is set to 90

degrees. And finally, any edge direction falling within the red range (112.5 to 157.5 degrees) is

set to 135 degrees.

Step5

After the edge directions are known, non maximum suppression now has to be applied.

Nonmaximum suppression is used to trace along the edge in the edge direction and suppress any

pixel value (sets it equal to 0) that is not considered to be an edge. This will give a thin line in the

output image.

Step6

Finally, hysteresis is used as a means of eliminating streaking. Streaking is the breaking up of an

edge contour caused by the operator output fluctuating above and below the threshold. If a single

threshold, T1 is applied to an image, and an edge has an average strength equal to T1, then due to

noise, there will be instances where the edge dips below the threshold. Equally it will also extend

above the threshold making an edge look like a dashed line. To avoid this, hysteresis uses 2

thresholds, a high and a low. Any pixel in the image that has a value greater than T1 is presumed

to be an edge pixel, and is marked as such immediately. Then, any pixels that are connected to

this edge pixel and that have a value greater than T2 are also selected as edge pixels. If you think

of following an edge, you need a gradient of T2 to start but you don't stop till you hit a gradient

below T1.

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Fig 4.3: Canny edge image

After Canny Edge we will be performing the GAMMA Adjustment:

Values in the range 0-1 enhance contrast of bright regions; values > 1 enhance contrast in dark

regions.

4.3.2 HOUGH TRANSFORM

For identifying the feature & shapes specifically eclipse, circle.

The Hough transform is a feature extraction technique used in image analysis, computer vision,

and digital image processing. The purpose of the technique is to find imperfect instances of

objects within a certain class of shapes by a voting procedure. This voting procedure is carried

out in a parameter space, from which object candidates are obtained as local maxima in a so-

called accumulator space that is explicitly constructed by the algorithm for computing the Hough

transform.

The classical Hough transform was concerned with the identification of lines in the image, but

later the Hough transform has been extended to identifying positions of arbitrary shapes, most

commonly circles or ellipses.

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We will be using a scaling factor & multiply it with the radius (0.4) to speed the hough

transform. Here we will be considering a range of iris & pupil radius provided in research from

the Chinese University.

Then border of the inner & outer circles are marked with white lines & the other noisy image

with eyelids is extracted.

4.4 IMAGE NORMALIZATION

Image is then normalized & the patterns of circle IRIS, circle PUPIL & Noise is written in

an pattern

Once the iris region is segmented, the next stage is to normalize this part, to enable generation of

the iriscode and their comparisons. Since variations in the eye, like optical size of the iris,

position of pupil in the iris, and the iris orientation change person to person, it is required to

normalize the iris image, so that the representation is common to all, with similar dimensions.

Normalization process involves unwrapping the iris and converting it into its polar equivalent. It

is done using Daugman’s Rubber sheet model. The center of the pupil is considered as the

reference point and a remapping formula is used to convert the points on the Cartesian scale to

the polar scale.The modified form of the model is shown below.

Fig 4.4: Normalization process

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where r1 = iris radius

The radial resolution was set to 100 and the angular resolution to 2400 pixels. For every pixel in

the iris, an equivalent position is found out on polar axes. The normalized image was then

interpolated into the size of the original image, by using the interp2 function. The parts in the

normalized image which yield a NaN, are divided by the sum to get a normalized value.

Fig 4.4: Unwrapping the iris

Fig 4.4: Normalized iris image

4.5 ENCODING

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The final process is the generation of the iriscode. For this, the most discriminating feature in

the iris pattern is extracted. The phase information in the pattern only is used because the phase

angles are assigned regardless of the image contrast. Amplitude information is not used since it

depends on extraneous factors. Extraction of the phase information, according to Daugman, is

done using 2D Gabor wavelets. It determines which quadrant the resulting phasor lies using the

wavelet

An easier way of using the Gabor filter is by breaking up the 2D normalized pattern into a

number of 1D wavelets, and then these signals are convolved with 1D Gabor wavelets.

Gabor filters are used to extract localized frequency information. But, due to a few of its

limitations, log-Gabor filters are more widely used for coding natural images. It was suggested

by Field, that the log filters (which use gaussian transfer functions viewed on a logarithmic scale)

can code natural images better than Gabor filters (viewed on a linear scale). Statistics of natural

images indicate the presence of high-frequency components. Since the ordinary Gabor fitlers

under-represent high frequency components, the log filters become a better choice.

LogGabor filters are constructed using

Since the attempt at implementing this function was unsuccessful, the gabor- convolve function

written by Peter Kovesi was used. It outputs a cell containing the complex valued convolution

results, of the same size as the input image. The parameters used for the function were:

nscale = 1

norient = 1

minwavelength = 3

mult = 2

sigmaOnf = 0.5

Using the output of gaborcovolve, the iriscode is formed by assigning 2 elements for each pixel

of the image. Each element contains a value 1 or 0 depending on the sign + or – of the real and

imaginary part respectively. Noise bits are assigned to those elements whose magnitude is very

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small and combined with the noisy part obtained from normalization. The generated IrisCode is

shown below.

                   

                                                                   Fig 4.5: Iris Code image

4.6 CODE MATCHING

Patterns are then matched using hamming distance to find the similarity.

HAMMING DISTANCE:

The Hamming distance between two strings of equal length is the number of positions at which

the corresponding symbols are different. Put another way, it measures the minimum number of

substitutions required to change one string into the other, or the number of errors that

transformed one string into the other.

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Fig 4 : Process Flow Diagram

Chapter 5

Details of Hardware and Software

The software used to develop the system is as follow:

MATLAB

MATLAB® is a high-level technical computing language and interactive environment for

algorithm development, data visualization, data analysis, and numeric computation. Using the

30

MATLAB product, you can solve technical computing problems faster than with traditional

programming languages, such as C, C++, and FORTRAN.

You can use MATLAB in a wide range of applications, including signal and image processing,

communications, control design, test and measurement, financial modeling and analysis, and

computational biology. Add-on toolboxes (collections of special-purpose MATLAB functions,

available separately) extend the MATLAB environment to solve particular classes of problems in

these application areas.

MATLAB You can integrate your MATLAB code with other languages and applications, and

distribute your MATLAB algorithms and applications.

Key Features

High-level language for technical computing

Development environment for managing code, files, and data

Interactive tools for iterative exploration, design, and problem solving

Mathematical functions for linear algebra, statistics, Fourier analysis, filtering,

optimization, and numerical integration.

2-D and 3-D graphics functions for visualizing data

Tools for building custom graphical user interfaces

Functions for integrating MATLAB based algorithms with external applications and

languages, such as C, C++, FORTRAN, Java, COM, and Microsoft Excel.

Adobe Flash

Adobe Flash (previously called Macromedia Flash) is a multimedia platform created by

Macromedia and currently developed and distributed by Adobe Systems. Since its introduction

in 1996, Flash has become a popular method for adding animation and interactivity to web

pages; Flash is commonly used to create animation, advertisements, and various web page

components, to integrate video into web pages, and more recently, to develop rich applications.

31

Adobe Photoshop

Adobe Photoshop is a professional image editing software package that can be used by experts

and novices alike. While this handout offers some very basic tips on using the tools available in

Photoshop, more comprehensive guidance can be accessed on the web or in the help menu of

your version of Photoshop.

Microsoft Access

Microsoft Office Access, previously known as Microsoft Access, is a relational database

management system from Microsoft that combines the relational Microsoft Jet Database Engine

with a graphical user interface and software development tools. It is a member of the 2007

Microsoft Office system.

Access stores data in its own format based on the Access Jet Database Engine. It can also import

or link directly to data stored in other Access databases, Excel, SharePoint lists, text, XML,

Outlook, HTML, dBase, Paradox, Lotus 1-2-3, or any ODBC-compliant data container including

Microsoft SQL Server, Oracle, MySQL and PostgreSQL. Software developers and data

architects can use it to develop application software and non-programmer "power users" can use

it to build simple applications. It supports some object-oriented techniques but falls short of

being a fully object-oriented development tool.

Chapter 6

Design Details

6.1 DATA FLOW DIAGRAM

A data flow diagram (DFD) is a graphical representation of the "flow" of data through an

information system. DFDs can also be used for the visualization of data processing.

32

LEVEL 1

Fig 6.1: Data Flow Diagram of Iris based Security System 

33

database

user_iduser_namuser_genderiris_image

stores_user_details()store_patterns()

user

image_iddetails

provides_image()iris_image()

System

admin_idadmin_pwd

pattern_matching()generates_iris_pattern()searching_iris()acquisition()segmentation()normalization()

Admin

Adim_id

updates_user_details()checks_user_details()search_iris()

Fig 6.2: Class Diagram

database

user_id=I1001user_nam=Ramuser_gender=maleiris_image=111

stores_user_details()store_patterns()

user

image_id=I1001details=Ram

provides_image()iris_image()

System

admin_id=akhiladmin_pwd=akhiliris

pattern_matching()generates_iris_pattern()searching_iris()acquisition()segmentation()normalization()

Admin

Adim_id=akhil

updates_user_details()checks_user_details()search_iris()

34

Fig 6.3: Object Diagram

Add new person

Update user

User org details

Search

User

Admin

User provides image

Fig 6.4: Usecase Diagram

35

Login

Login Successful

Invalid Admin

Valid Admin

Invalid Admin

Main Page

search_user

ADD_new_user_details

Accept_iris_image

image_acquisition

Segmentation

Normalization

Feature_Encoding

Pattern Stored into DB

Matching patterns

Detailed display

Yes exist

Dosen't exist

not found

Present

Fig 6.5: Activity Diagram

36

Enter Image

Segmentation,Normalization,Localization

Database

Start

Matched Image

Display Record

Exit

Apply Algo

Search

Generated Iris Pattern

Search In DatabaseFound

Fig 6.6: State chart diagram

View Class

Login Form.m

Searching Form.m

Add new person.m

Edit form.m

Add criminal code.m

Add new organisation.m

Access class

Database

Fig 6.7: Component Diagram

37

DB

Keyboard

Iris System

Fig6.8: Deployment Diagram

38

Admin User System DB

Login Admin id & pwd

Succesful login

Enter details

Stores details

iris image not presentProvide image

accept iris image

provides image

Pattern Generator

Stores pattern

updates DBLogout

successfully logged out

Fig 6.9: Sequence Diagram

39

Admin

User

System

DB

9: Pattern Generator

11: updates DB

1: Login Admin id & pwd7: accept iris image

12: Logout

2: Succesful login13: successfully logged out

3: Enter details8: provides image

6: Provide image

4: Stores details10: Stores pattern

5: iris image not present

Fig 6.10 Collaboration Diagram

40

Chapter 7

Implementation Plan for next semester

Login Process

Identifier and name Login Process

Initiator Admin User

Goal To log into the system

Pre-condition The system is running, the user is added as admin

into the system.

Post-condition The user is successfully able to log in into the

system

When admin wishes add, edit or do some processing then need to login, the user

supplies his credentials to the system. The system can successfully verify the user

supplied credentials. The system can successfully allow the user to perform various

admin level tasks as follow:

Adding new person detail.

Editing person detail.

Searching person information.

Adding new organization detail.

Adding new institution detail.

41

Add new person detail

Identifier and name Add new person

Initiator Admin User

Goal The system should be able to add new user.

Pre-condition The system is running

Post-condition The admin is successfully able to add new personal

information to the system.

In order to add new person to the system. The admin needs to fill up the forms like

Personal Information, Contact Detail, and Health Detail etc. in the system. The

system verifies wheather a person with same or similar iris pattern exists. If it

doses it gives the detail of persons having same or similar iris pattern and admin

can take necessary step.

Edit the person detail

Identifier and name Edit the person detail

Initiator Admin User

Goal The System should allow the admin to edit the

information about person exist in the database

Pre-condition The system should be running

Post-condition The admin should be successfully able to edit person

detail.

42

In order to edit person that exist in the system, admin needs to refill up the forms

like Personal Information, Contact Detail, and Health Detail etc. expect iris pattern

in the system. The system verifies weather a person with same or similar iris

pattern exists. If it doses it gives the detail of persons having same or similar iris

pattern and admin can take necessary step.

Search Process

Identifier and name Search Process

Initiator Admin User

Goal The System should allow the admin to find the

person in system.

Pre-condition The system should be running.

Post-condition The user should be successfully able to find the

person information by proving necessary credential.

When admin wish to get the information about a particular person then admin

need to provide necessary credential to get the information and admin would b

able to get all the information about the person.

Add institutional detail

Identifier and

name

Add institutional detail

Initiator Admin User

Goal The System should allow the admin to add new institution

in the system.

Pre-condition The system should be running.

43

Post-condition The admin should be successfully able to add new in

institution the system.

When institution list is updated with new names, the admin is able to update the

same in the system so that it can be available to use.

Add organization detail

Identifier and

name

Add organization detail

Initiator Admin User

Goal The System should allow the admin to add new

organization in the system.

Pre-condition The system should be running.

Post-condition The admin should be successfully able to add new

organization in the system.

When organization list is updated with new names, the admin is able to update the

same in the system so that it can be available to use.

GANTT CHART

Gantt charts allow you to access how long a project should take.

Gantt charts lay out the order in which tasks need to be carried out.44

Gantt charts help manage the dependencies between tasks.

Gantt chart is useful way to prevent the general flow of a project’s tasks. Such charts are

particularly useful for coordinating multiple activities.

P- Planning A- Analysis D- Design

T- Testing I- Implementation

45

YEAR 2011 2012

No. WORK TASK AUG SEPT OCT

1 PLANNING P P P P P P

2 ANALYSIS A A A A A A

3 DESIGN

PHASE

D D D D D

4 CODING

5 DEBUGGING

6 TESTING

7 IMPLEMENT

ATION

References

Bibliography

[1] K. Ryoung Park and J. Kim, “A real-time focusing algorithm for iris

recogntion camera,”

IEEE Trans. Syst., Man Cybern. C, Cybern., vol. 35, no. 3, pp. 441–444,

Aug. 2005.

[2] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces:

Recognition using class-specific linear projection,” IEEE Trans. Pattern Anal. Machine

Intell, vol. 19, pp. 711–720, July 1997.

Websites

[1] http://en.wikipedia.org/wiki/Iris_recognition

[2] http://www.cl.cam.ac.uk/~jgd1000/

[3] http://www.nist.gov/itl/iad/ig/ice.cfm

Papers

P. Phillips, W. T. Scruggs, A. J. O’Toole, P. J. Flynn, K.W. Bowyer, C. L. Schott, and M.

Sharpe, “FRVT 2006 and ICE 2006 large-scale results,” Nat. Inst. Standards Technol., 2007.

[Online].

46

J. R. Matey, O. Naroditsky, K. Hanna, R. Kolczynski, D. J. LoIacono, S. Mangru, M. Tinker, T.

M. Zappia, and W. Y. Zhao, “Iris on the move: Acqutision on images for iris recognition in less

contrained environments,” Proc. IEEE, vol. 94, no. 11, pp. 1936–1947, Nov. 2006.

47