accurate biometrics authentication technique based on fast, robust iris recognition system using...

7/31/2019 Accurate Biometrics Authentication Technique based on Fast, Robust Iris Recognition System using Enhanced Quali

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1. INTRODUCTIONHuman eye is an organ that works with the brain and

provided us vision. The colored part of the eye lies

between the cornea and lens consists of connective

tissues and smooth muscle fibers known as iris and

that is used for person identity verification based on

biometrics system. The confirmation of the true

identity of a person is one of the biggest challenges in

todays information technology world; number of

systems has to compromise because of this challenge.

There is need of authentication of personal identity to

access the confidential and secure system.

Authentication plays an important role as it is the

basic line of defense against the intrusion attacks.

Authentication is provided by traditional methods

and biometrics method. Biometrics consists of the

methods for uniquely recognizing the human identity

by physical or behavioral characteristics. Physical

characteristics are related to the shape of the body for

example finger prints, face recognition and iris

recognition while behavioral characteristics are

related to the behavior of the person for example

signatures, keystrokes and voice. Probability of

variation in behavioral characteristic is more than in

physical characteristic because it is easy to copy the

signatures and voice as compared to the face, finger

prints and iris of a person.

Traditional methods include passwords, identity

cards or token. Among all biometrics methods based

on physical characteristics iris recognition is the most

safe and secure identification and authentication

technique. As iris is the internal organ of the eye and

the basic feature of human iris is that the color, texture

of each humans iris is unique and stable. Iris is

protected from the surrounding environment and

remains same till its life time so because of its stability

and reliability it becomes the most consistent and

secure feature for person identification and

verification and most importantly human iris is the

best feature for recognition and authentication.

The basic concept of this study is to develop the

system which does identification (one-to-manytemplate matching) and verification (one-to-one

template matching) of a person. It is the system which

provides the high level security to the person based

on automated iris recognition. The main objective of

the system is that to secure private and off the record

data and information and it is not easily accessible to

anyone. People can feel secure because only the

identified and verified person can access the system.

Accurate Biometrics Authentication Technique based on Fast,

Robust Iris Recognition System using Enhanced Quality Image

Zoama Afaq, Bushra Sikander,and Malik Sikander Hayat Khiyal

AbstractThe main aim of this research is to develop biometrics authentication system with high accuracy based on human iris

recognition using enhanced quality image. An iris recognition system has been developed using pattern recognition techniquesbased on templates i.e. the images have been captured from the live video generated from high resolution camera and has been

stored as the Dataset. Additionally a collection of different eye images have been stored in separate database that have been used

for comparison and matching for accurate identification and verification phenomenon. Iris identification and authentication is

thus based on individuals eye. Authentication has been done with low error rate i.e. False Acceptance Rate (FAR) and False

Rejection Rate (FRR). 1D Log Gabor Filters have been used for feature extraction and hamming distance has been used for

template matching that also provides low FAR. In this way a fast and robust personal identification and verification system

has been developed which gives the precise and accurate results. Thus the proposed system provides the secure, efficient and

user friendly interface to the person.

Index Terms: Biometrics Authentication, Iris Recognition, False Acceptance Rate, False Rejection Rate, Iris Identification.

u

Zoama Afag is an under graduate student of Department of Software

Engineering, Fatima Jinnah Women University The Mall, Rawalpindi,

Pakistan.

Bushra Sikander is Lecturer at the Department of Computer Sciences,

Fatima Jinnah Women University The Mall, Rawalpindi, Pakistan.

Dr. Malik Sikandar Hayat Khiyal is Professor and Head of Academic

(ES), APCOMS, Khadim Hussain Road, Lalkuti, Rawalpindi, Pakistan.

JOURNAL OF COMPUTING, VOLUME 4, ISSUE 6, JUNE 2012, ISSN (Online) 2151-9617

https://sites.google.com/site/journalofcomputing

WWW.JOURNALOFCOMPUTING.ORG 56


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There are various biometrics methods for the system

security and user identification that contain some

strong and weak points on the basis of which the

project is developed. There are real systems that

recognize the personal identification but some with

the errors that must be minimized for proper and

accurate system.

User acceptance is the important issue in biometrics

technology and we also know that various biometrics

techniques and systems exist that confirm the person

identity but the identification and authentication

system based on human iris gives the satisfactory

results. As iris is the unique organ of the eye can be

used for person identification by extracting the iris

pattern from the digital image of the eye of a person.

Digital camera is used to acquire the image, the user

must be look straight into the camera so that theaccurate image is captured which is further processed

and used for pattern is matching. Pattern matching is

done with the iris images already stored in the

database for identification and authentication of the

legitimate user. It requires the accurate captured

image in which the persons eyes are opened in a way

that show the exact iris part however eyelashes and

eyelids are included as a noise. For this purpose user

is allowed to view in the camera for a few seconds in

order to store the some images as if one image is not

captured perfectly then the system rejects that image

and take the next one for the comparison. The Iris

recognition is the most reliable and accepted system

among all biometrics system as iris is more stable and

easy to compare with the other iris images with less

error rate. Following are the errors that occur during

iris verification process. When an authorized person is

rejected by biometrics system then this is known as

type I error also called as false rejection rate (FRR) [1].

When an unauthorized person is incorrectly accepted

by the biometrics system then this is referred to type II

error also known as false acceptance rate (FAR) [1].

The percentage rating of false rejection rate versusfalse acceptance rate is crossover error rate (CER). The

accuracy of the system is inversely proportional to the

crossover error rate i.e. lower CER means better

accuracy of the system [1]. There are various

applications of iris recognition system some of them

are described here. Iris recognition system can be

deployed at airports as the current security system at

the Amsterdams Schiphol Airport. The currently

existing system at Amsterdams airport is by Schiphol

Group which verifies a persons identity and also

used for border passing functions. Schiphol group is

an airport operator in the Netherlands. The current

AUTOMATIC BORDER PASSING (ABP) system [2]

runs on the IBM server and uses iris scan technique

for travelers identification by template matching

using pre-registered iris data, stored on encryptedsmart card. This system provides the high security to

all the passengers involves identifications in function

such as ticketing, checking and boarding. At

Charlotte/Douglas Airport in North Carolina a

biometric system based on iris recognition is

providing the security and safety from the intruders

and is designed by Eye Ticket Corporation in Virginia

[3]. An individuals iris code is recorded with the help

of a black and white video camera and then the image

is stored in the database of enrolled user for

authentication. This system involves revolving doorways; in the first door the iris recognition is done and

in the next door confirmation of user identity is done.

IRISPASS-S Gate Management system [4] has been

installed in Japan at many locations that fulfills the

security needs in different companies and

government data centers where there is need to secure

the confidential information from the intruders. In

Japan, Oki Electric Industry Corporation Limited

introduced a new iris recognition system which is

IRISPASS-WG system [4]. It contains the enhanced

user-operability which involves the automatic

detection of the human eye. The system consists oftwo devices named as management device and

registration device; both are installed at the entrance.

Iris recognition system also provides security at the

national border controls, for logon to computer, for

credit card authentication, for anti-terrorism, for

secure financial transactions, for internet security and

hence in many other areas [5].

The remaining part of this paper covers the proposed

system design and implementation phase. In section 2

the reviewed literature is described. In section 3modeling problem and system design is elaborated, in

section 4 experimental results are illustrated and in

last section conclusion and future work is

summarized.

2. LITERATURE REVIEW





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Ganeshan et al., [6] has developed a biometric

technique for iris recognition and identification that

provides remote examination based on the multi-scale

representation to capture the image at varying spatial

scales. And it is achieved by Laplacian of Gaussian

filter named as LoG filter. It is an advanced research

of biometrics where iris recognition is fast and costeffective. Localization and alignment technique used

in this research paper is unique and frames the

subjects iris in efficient manner. It provides accurate

results and enhanced features of iris recognition.

Multi-scale representation is an efficient technique

explained in this paper. The drawback in this research

is that the rejection process is not explained in this

paper i.e. what if the irises are not matched or

matched with error. And also it is not for closed eyes.

If the iris scan is done remotely, then the certainty of

authentication is lower.

Chen et al., [7] has developed the Automatic Iris

Recognition System (AIRS). The system authenticates

the right person and rejects the fake person. The main

focus of the researcher is on extracting the unique iris

features from iris images. When we use 2D Gabor

wavelet transform then the visible texture of humans

iris is encoded in a sequence of multi scale 2D Gabor

wavelet coefficients and whose most significant bit

consisting of 256 byte code that is called iris code. Iris

code is used to recognize individuals using fractal

dimensions. The existing AIRS system described by

the author generates the iris feature code and two

matching techniques have been used namely K-means

and Neural Networks and they have been applied on

the iris feature code to identify the iris pattern. The

existing system is performed well for high security

using first method. In first method original domain is

used while in second method wavelet domain is used

for feature extraction. We conclude from our results

that wavelet domain is superior to original image

domain. Thus, this is the accurate system for FAR. The

problem appeared in the research of AIRS is that itprovides the low FAR but it does not provide the low

FRR.

Dong et al., [8] has make iris recognition easier by

proposing iris recognition systems with efficient

human computer interface and with two different

strategies, one meets the requirements of low end

market and other meets the requirements of high end

market. As to make iris recognition comfortable this

research covers the two iris recognition system; one is

double eye device based on LCD screen feedback like

a magic mirror and other is long distance device on a

high resolution camera, long focus lens and pan tilt

zoom unit. . For image acquisition iris image is done

using digital sensor through an optical lens. Digital

sensors CCD and CMOS are used. People can performiris recognition from 3 meters away. Thus,

Recognition algorithm used in this research has been

very fast, robust and accurate. Two types of iris

recognition system will satisfy the markets need. All

available methods are integrated to make the

comfortable iris recognition system. This paper does

not cover the verification phenomenon. This system

only covers the image acquisition phenomenon which

is helpful for the people engaged in the development

of iris recognition system.

Vatsa et al., [9] has developed the system to improve

the accuracy and speed of iris recognition. Iris images

are captured in a controlled environment to ensure

high quality. This research effort focuses on reducing

the false rejection. Iris textural and topological feature

has been extracted using 1D log polar Gabor

transform. The masked polar image thus converted to

binary images. To verify a persons identity, the iris

template has been matched with the stored templates.

Topological feature extraction has been done using

Eulers method. 2-SVM method has been used to

achieve low error rate. In this research ICE 2005

database, CASIA version3 database and UBIRIS

database have been used. The Algorithms used for

validation is Daugmans Integro Differential and 2-

SVM algorithm. Results of this system have shown the

improvement in the performance of iris verification

and identification and it shows accurate non ideal iris

segmentation using modified functions. Thus the

proposed algorithm reduces the false rejection rate by

maintaining the low false acceptance rate. This system

requires much effort and one module will affect the

other in case of performance and accuracy. It is timeconsuming and expensive as well.

Vrcek et al., [10] has covered the personal verification

system based on iris pattern by removing the noise

and sobel edge detection operator has been used. The

iris pattern has been converted to fix two dimensional

spaces based on Dougmans model. The author has

used the 2D Gabor wavelet method for feature

extraction. Then Hamming Distance (HD) has been





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calculated using XOR. It gives accurate results for

false acceptance rate but not for false rejection rate.

For real test system the results that are achieved, are

satisfactory.

By the literature review it is come to know that the

existing research papers lack the high quality image,low error rates and high accuracy for iris recognition

and authentication. So, to overcome these drawbacks

there is need to propose a better system so the main

purpose of this research is to develop a biometrics

system based on iris recognition and authentication

that is also able to retain high accuracy with less

computational time by maintaining the high quality

regions unchanged and improving the low quality

regions of the iris image.

3. SYSTEM DESIGN

First of all there is need to explain the actual problem

related to the system on the basis of which proposed

system is implemented.

3.1.Statement of the modeling problem

In order to achieve the accurate results for the iris

recognition and verification system it becomes

necessary to design and implement the general andefficient system. There are various existing systems

for static images but here the problem needs to be

solved is that to recognize and verify the human iris

for dynamic images and thus the system has been

based on biometrics authentication technique. By

following the above mentioned steps the resultant

solution is as follows:

Resultant Image= (IV(TM (FE (IS (M (E (A (B (U (G (R

(I))))))))))))

Where the symbols are described in the table 1.

Table 1. List of factors

Symbols Description

R Read Image

U Uint8 Conversion

G Grayscale

I Image to be processed

B Binary

A Average Filter

E Edge Detection

M Morphological Operation

IS Iris Segmentation

FE Feature Extraction

TM Template MatchingIV Iris Verification

On the basis of some problems in iris recognition

system, discussed previously, a new model has been

proposed and the design of the proposed system is

described in this section. Thus, the system is

elaborated in the following steps.

3.2. Image Acquisition

First of all images have been captured from a videoand frames are stored in a permanent storage in JPG

format with size 280x320. These frames have been

retrieved one by one and then used for pre-processing

and pattern matching after applying various

techniques for image processing. Frames captured

from the video are in RGB format and these frames

have been converted into grayscale. The images have

been stored in the variable that consists of the array

elements. Gray scale image has been changed to uint8

image. Uint8 image has been transformed to binary

image.

3.3. Image Pre-processing

Pre-processing consists of various operations on

image. These operations does not increase the image

information and contents but helps to decrease and

remove the unwanted information from the image.

Pre-processing operations include the image

cropping, image filtering, edge detection, intensity

adjustment, threshold control, histogram equalization

and many other processes. The specific pre-processing

operations performed here are:

A. Smoothing:

To retrieve the wanted details from the acquired

image there is need to perform some operations on

the image and first of all it is necessary to hide the low

level detail i.e. unnecessary details. Filters are used for

smoothing process and there are various filters for

this purpose. They give the blurring effect to the





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desired level and it depends on the masking value of

filter. Some of the low pass filters used for smoothing

are Gaussian low pass filter, disk, median filter,

maximum filter, minimum filter and average filter.

Average filter is the linear smoothing filter while

minimum, maximum and median filters are non

linear smoothing filters. Average filter has by defaultfilter mask of [3 3] by increasing this vector value the

image has become more blur and its selection depend

on the system requirement. The purpose is to hide the

low level detail in order to get the desired results. The

average filter gives the better and efficient required

results than other low pass filters. Average filter is

given by:

Ones (n (1), n (2))/ (n (1)*n (2))

It creates a square matrix of ones having n dimension,

where n is any integer normally the matrix is of [3 3].

B. Edge Detection:

After the smoothing process the edges of the image

has been detected using the filters that are used for

edge detection. More the detail and noise in the image

more will be the detail in edge detected image. If the

image has been blurred up to the certain level then it

helps to show the less level of detail located inside the

main edges of the image, which is basically referred toas noise. There are several 2D filters used for edge

detection namely canny filter, zero cross method,

laplacian filter, laplacian of Gaussian filter i.e. Log

filter, Roberts filter, unsharp filter, prewitt filter and

sobel filter. Canny filter is another type of filter used

to detect the edges and it reduces the less detail so the

canny filter produces the better results for the

proposed system. Although for binary images all the

filters give the same result but for the gray scale

images their result vary.

C. Morphological Operations:

To identify the object within the image is the difficult

task and various morphological operations have been

used for this purpose. Some of them have been used

in the proposed system. Strel and dilation has

performed and dilated image depends on the value of

the pixels specified in the parameter. After the

dilation holes in the dilated image have been filled

that helps to detect the center of the pupil and iris.

D. Boundary Cropping:

By applying the average filter the boundary of the

image has become prominent and that is unnecessary

for the image processed further to derive the center

point for pupil and iris. So the boundary of the dilated

image has been cropped manually.

3.4. Generation of High Quality Image:

After applying smoothing, edge detection filters and

morphological operations the resultant image has

been enhanced and of high quality.

3.5. Iris Segmentation:

After getting the enhanced image there is time to get

the pupil and iris. First of all pupil center has been

detected by using the binary image. As in the binary

image the pixel value is either 0 or 1. 0 represents the

black area in the image while 1 represents the white

area in the image. After pre-processing phase, in the

proposed system, the binary image has the smooth

pupil and almost no noise. The column that has

maximum number of ones has been retrieved first.Then the index of that column and its corresponding

row has been achieved. The consecutive number of

ones in that column has been noticed and the row,

that contains the first one among all the consecutive

ones, has been get. From starting and ending point of

the maximum ones column the midpoint has been

calculated and actually that is the row that has the

center point. Thus the row and column has been

achieved which is the x and y coordinates of the pupil

respectively. Thus the center point of the pupil and

radius of the pupil has been calculated. Using thatcenter point the circle around the pupil boundary has

been drawn and in this way the pupil has been

segmented. In the same way iris is segmented. In the

proposed system the iris and pupil is almost

concentric so their centers are also same but their

radius are different and varying from image to image

as the pupil size varies. With the help of iris radius the

circle has been drawn around the iris boundary.





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Hence, the pupil and iris has been segmented

properly and efficiently.

Apart from above all there is a possibility that the

person eye is closed, in that case error can occur and

in order to make the system error free, a solution has

been proposed and implemented.

3.6. Iris Normalization:

The segmented image has been converted from

circular form to rectangular form as the pupil size

varies from frame to frame so the iris region has been

saved in rectangular form that is accurate method for

better feature extraction and pattern matching. This

also works in the loop i.e. the loop is from outer pupil

boundary to the iris outer boundary and the in

between region of both the pupil and iris boundarieshas been achieved. Each coordinate has been retrieved

from the circular form and mapped to the rectangular

form one by one. Thus the normalized image has the

polar coordinates.

3.7. Feature Extraction:

After the iris normalization has been done there is

time to extract the iris features for template matching

and comparison. There are various methods that are

used for feature extraction namely Wavelet encoding,Gabor filters, Haar wavelet and Log Gabor filter.

However encoding based on Fourier transform was

replaced by wavelet encoding because it gives the

better and more effective results and also reduces

signal to noise ratio. Gabor filter is for one

dimensional signals as well as for two dimensions,

first one is known as 1D Gabor filter and second one is

called as 2D Gabor filter and mainly 1D Gabor filter

provides excellent band pass filtering. The Log Gabor

filter provides the logarithmic function in order to

remove the DC component of medium high pass

filter. So the 1D Log Gabor filter has been used for

feature extraction in the proposed iris recognition

system and it is given by:

Where f gives the frequency of Log Gabor Filter, f0represents the centre frequency and is the

bandwidth of the filter.

3.8. Template Matching:

Before the matching process there must be some otherimages of different irises of the persons in the separate

database so that at the time of comparison images are

retrieved one by one. So for this purpose images have

been captured from the different live videos and have

been stored in the dataset. The all processing steps,

illustrated previously, have been applied on all the

images in the new database and thus the images from

both the database have been retrieved for comparison

as it contains different type of iris images so that the

efficient desired results have been produced in case of

individuals identity.

Although on the basis of feature extraction comparing

templates have been created and it involves these two

cases:

If the metric gives the same range of values

for both created templates from the same

person eye then this is referred to as Intra

Class Distribution.

If the metric gives the different range of for

the templates created from different irises

then this is called Inter Class Distribution.

Hamming Distance, Euclidean Distance and

Normalized Correlation are the methods that are used

for template matching but here the proposed method

is Hamming Distance and it gives efficient results. As

templates based on iris codes have been generated in

the previous step that have been used in this step and

thus hamming distance is the method for the

comparison of iris codes generated either from the

same irises or from iris of different persons. It is stated

as:

Here code A and code B are two iris codes, with

corresponding bit masks, mask A and mask B.

represents the XOR operator whereas representsthe AND operator. [11]





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3.9. Iris Verification:

The iris Verification process is mainly based on the

previous steps i.e. Hamming Distance. Two results

have been concluded from this step. If the templatesare matched then the person identity will be verified,

otherwise it will display the message that both eye

images are of different persons.

4. EXPERIMENTAL RESULTS

As the system design has explained in the previous

section now its time to show the results of proposed

processing and logic described earlier. In this research

main task is of image processing and the tool used for

this purpose is MATLAB. MATLAB is theabbreviation of Matrix Laboratory [12] and it is the

numerical computing and fourth generation language.

Basically MATLAB deals with matrix and arrays and

thus MATLAB is a high level matrix language. All

data is stored in the form of arrays and matrices and

additionally there is no need to declare the dimension

of the arrays. Thus MATLAB is the technical

computing language that contains the combination of

visualization, computation and programming

environment. MATLAB is also accurate in numerical

calculations and applications.

As it is widely used in computer vision and image

processing so new algorithms are being implemented

in the MATLAB as certain functionalities are only

available in the MATLAB. Thus it has the modern

programming environment including the data

structures, object oriented programming and

debugging tools.

To implement the system design MATLAB 7.9.0 is

used. First of all image has been captured from the

video. Sample image is given in figure 1:

Figure 1. Original RGB image

Then the acquired RGB image has been converted to

the grayscale image as shown in the figure given

below:

Figure 2. Gray Scale Image

The grayscale image is then converted to uint8 and

then binary image and the result shows as follows:

Figure 3. Binary Image

Then the next step shows the results of [16 16] averagefiltered image and then edge detected image using

canny filter has been obtained as shown in figure 4

and figure 5:





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Figure 4. Average Filtered Image

Figure 5. Edge Detected Image

Some morphological operations have been performed

on the edge detected image as shown below in figure

6 and figure 7:

Figure 6. Dilated Image

Figure 7. Hole Filled Image

After the image preprocessing the above described

logic of iris segmentation has performed on the holes

filled image to detect the pupil center. The midpoint is

calculated from starting and ending point of the

consecutive ones in the processed image.

mid_point= floor ((starting point + ending point)/2);

By the midpoint radius is easily calculated by

subtracting the midpoint either from the starting point

or ending point and the center mapped binary and

gray scale image is shown in figure 8 and figure 9

respectively:

Figure 8. Center Detected Binary Image





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Figure 9. Center Detected Gray Scale Image

Then to detect the pupil and iris boundary circle is

drawn around pupil outer boundary and iris outer

boundary. Thus to draw the circle following formula

is implemented.

re = round(2 * pi * radius);

step = 2 * pi / re;

Then for loop is executed from 0 to 2pi with the

increment of step and in that loop x and y is

calculated and wit the help of those x and y

coordinates and radius. Circle is drawn, using the

below mentioned formula, around pupil and iris as

shown in figure 10 and 11 respectively:

circle_x=round(midpoint+pupil_radius*cos (j));

circle_y=round(column_index+pupil_radius*sin (j));

Here circle_x and circle_y are the coordinates that

have been get in the loop where j is the loop variable.

For circle around the iris boundary pupil_radius will

be replaced by iris_radius as,

circle_x=round (midpoint+iris_radius*cos (j));

circle_y=round(column_index+iris_radius*sin (j));

Figure 10. Pupil Segmented Image

Figure 11. Iris Segmented Image

The next step is to normalize the iris and this is done

by calculating value of theta in order to get coordinate

on iris and pupil boundaries and then calculate the

pixel coordinated at angle theta i.e. pupil x and y

coordinates and iris x and y coordinates, x and y is

computed using mathematical formula to get the

points from pupil towards iris or from iris towards

pupil as described below:

x_pupil= pupil_x + pupil_radius * sin(t); y_pupil=

pupil_y + pupil_radius * cos(t); x_iris= iris_x +

iris_radius * sin(t); y_iris= iris_y + iris_radius *

cos(t);

And from both directions the resultant images are as

shown in figure 12:

(a)





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(b)

Figure 12. Normalized Iris Images

Feature extraction is done using 1D log Gabor filter

using normalized iris. Before the template matching

process same processing is done on the other stored

images and then captured and stored images in the

dataset are compared on the basis of hamming

distance. If the hamming distance is less than 0.4 then

the images are of same person otherwise images are of

different pesons and if its value is zero then it shows

the perfect match. Templates of the above normalized

iris is shown in figure 13.

Figure 13. Binary Template of Normalized Iris

Similarly the same processing is done on the other iris

image of the same person and all the above mentioned

steps give the results as shown in the figures given

below:

Figure 14. (a) RGB Image

Figure 2. (b) Gray scale Image

Figure 3. (c) Binary Image

Figure 4(a) Average Filtered Image

Figure 5 (b) Edge detected Image

Figure 6 (c) Dilated Image

Figure 7 (d) Hole Filled Image





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Figure 8 (a) Center Detected Binary Image

Figure 9 (b) Center Detected Gray Scale Image

Figure 10(a) Pupil Segmented Image

Figure 117 (b) Iris Segmented Image

Figure 12(a) Normalized Iris Image

Figure 13 (b) Binary Template

The next step is to compare the captured images with

the stored images in the dataset and for this purpose

same processing is done on the stored images and the

final results of that images are shown in the figures

given below:

Figure 14 (a) c) Pupil segmentation

Figure 15 (b) Iris Segmentation on the Images in the

Dataset

Figure 19 c) Pupil segmentation

(a)

(b)

(c)

Figure 160. Iris Normalization of the Images in the

Dataset





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(a)

(b)

(c)

Figure 21. Binary Templates of the Images in the

Dataset

5. CONCLUSION AND FUTURE WORK

The biometrics authentication system based on human

iris features is the most secure authentication system

and this report represents an efficient iris recognition

system. The proposed system has been implemented

in MATLAB 7.9 that has many latest and useful built

in functions and commands and thus helps to

implement the various constructive image processing

techniques that have been used to implement the

better and accurate human computer interface. The

two major tasks involves in the development of iris

recognition system has been covered in this paper i.e.

Iris Segmentation and the Feature Extraction. Iris

Segmentation is the critical process involved in

development of iris recognition system because the

accuracy of the system mainly depends on the iris

segmentation. Problem is basically faced in the centerdetection step and on which the basis of pupil and iris

has been segmented. Then segmented iris has been

normalized and then 1D log Gabor Filter has been

used and after hamming distance calculation results

have been shown. Thus the proposed system will

verify the authentic and valid user only by accurate

template matching. It is also for the time when the eye

is closed or user blinks his eyes, it will prompt the

message in that case and next image has been taken

for the processing of iris identification and

authentication. Thus it has been concluded that thedeveloped system is the efficient and friendly user

interface system that provides the individuals

security with accuracy and low error rates.

Although the study that has been done is

comprehensive and sufficient in its own terms, but

there is always room for improvement as in this

project as well. However the accuracy matters a lot

than the fast system but to reduce the time

consumption the system can be implemented in C++

or C# as MATLAB is the integrated language. As the

series matching has been done in the proposed system

so parallel matching could reduce the time so for that

purpose FPGA can be used. Eye lid and eyelashes

detection phenomenon can be added to increase the

accuracy and efficiency of the developed system.Another extension to this project is that high

resolution camera can be used to play the video at the

run time and then frames are captured and stored in

the database.

REFERENCES

[1] http://www.scribd.com/doc/38156947/Report-of-

Iris-Recognition-by-Bharath-m.

[2] http://www03.ibm.com/press/us/en/pressrelease/7

75.wss.

[3] http://edition.cnn.com/2000/TECH/computing/07/

24/iris.explainer.

[4] http://www.oki.com/en/press/2002/z02011e.html.

[5] http://www.cl.cam.ac.uk/~jgd1000/applics.html.

[6] Balaji Ganeshan, Dhananjay Theckedath, Rupert

Young, Chris Chatwin, Biometric iris recognition

system using a fast and robust iris localization

and alignment procedure, Optics and Lasers in

Engineering, vol 44(1), pp 124, January 2006.

[7] Wen-Shiung Chen, Shang-Yuan Yuan, A Novel

Personal Biometric Authentication Technique

Using Human Iris Based on Fractal Dimension

Features, In proceedings of IEEE International

Conference on Acoustics, Speech, and Signal

Processing, ICASSP 2003, Hong Kong, Vol 3,

pages 201-204, April 6-10, 2003.

[8] Wenbo Dong, Zhenan Sun, Tieniu Tan, How to

make Iris Recognition Easier?, In proceedings of

IEEE International Conference on Pattern

Recognition, ICPR 2008, Florida, USA, pages 1-

4, December 8-11, 2008.

[9] Mayank Vatsa, Richa Singh, and Afzel Noore,

Improving Iris Recognition Performance UsingSegmentation, Quality Enhancement, Match Score

Fusion, and Indexing, IEEE Transactions on

System, Man, and CyberneticsPart B:

Cybernetics, pp 1021-1035, Vol. 38, No. 4, August

2008.

[10]Gorazd Vrcek and Peter Peer , Iris-Based Human

Verification System, In proceedings of

IEEE International Workshop on Systems, Signal



http://www.scribd.com/doc/38156947/Report-of-Iris-Recognition-by-Bharath-m.%20%20%20%20%20http://www.scribd.com/doc/38156947/Report-of-Iris-Recognition-by-Bharath-m.%20%20%20%20%20http://www03.ibm.com/press/us/en/pressrelease/775.wss.%20http://www03.ibm.com/press/us/en/pressrelease/775.wss.%20http://edition.cnn.com/2000/TECH/computing/07/24/iris.explainer.%20http://edition.cnn.com/2000/TECH/computing/07/24/iris.explainer.%20http://www.oki.com/en/press/2002/z02011e.html.%20http://www.cl.cam.ac.uk/~jgd1000/applics.html.%20http://www.scribd.com/doc/38156947/Report-of-Iris-Recognition-by-Bharath-m.%20%20%20%20%20http://www.scribd.com/doc/38156947/Report-of-Iris-Recognition-by-Bharath-m.%20%20%20%20%20http://www03.ibm.com/press/us/en/pressrelease/775.wss.%20http://www03.ibm.com/press/us/en/pressrelease/775.wss.%20http://edition.cnn.com/2000/TECH/computing/07/24/iris.explainer.%20http://edition.cnn.com/2000/TECH/computing/07/24/iris.explainer.%20http://www.oki.com/en/press/2002/z02011e.html.%20http://www.cl.cam.ac.uk/~jgd1000/applics.html.%20


13/13

and Image Processing, IWSSIP 2009, Chalkida,

Greece, pages 1-4, July 18-18, 2009.

[11]Libor Masek, Recognition of Human Iris Patterns

for Biometric Identification, B. Eng. thesis,

University of Western Australia, Australia, 2003.

[12] http://staff.iiu.edu.my/azami/MATLAB%20tutorial%20of%20fundamental

%20programming.pdf.

BIBLIOGRAPHY

Zoama Afaq is the under graduate student of

Department of Software Engineering in Fatima Jinnah

Women University the Mall, Rawalpindi, Pakistan.

Bushra Sikander is the Lecturer in the Department of

Computer Science in Fatima Jinnah Women

University the Mall, Rawalpindi, Pakistan. Her

qualification isMS-CS..

Dr. Malik Sikandar Hayat Khiyal is Head of

Academic (ES), APCOMS, Khadim Hussain Road,

Lalkurti, Pakistan. He served in Pakistan Atomic

Energy Commission for 25 years and involved in

different research and development program of the

PAEC. He developed software of underground flow

and advanced fluid dynamic techniques. He was also

involved at teaching in Computer Training Centre,

PAEC and International Islamic University. His area

of interest is Numerical Analysis of Algorithm,

Theory of Automata and Theory of Computation. He

has more than hundred research publications

published in National and International Journals and

Conference proceedings. He has supervised three PhD

and more than one hundred and thirty research

projects at graduate and postgraduate level. He is

member of SIAM, ACM, Informing Science Institute,

IACSIT. He is associate editor of IJCTE and coeditor of

the journals JATIT and International Journal of

Reviews in Computing. He is reviewer of the journals,

IJCSIT, JIISIT, IJCEE and CEE of Elsevier.



http://staff.iiu.edu.my/azami/MATLAB%20tutorial%20of%20fundamental%20programming.pdfhttp://staff.iiu.edu.my/azami/MATLAB%20tutorial%20of%20fundamental%20programming.pdfhttp://staff.iiu.edu.my/azami/MATLAB%20tutorial%20of%20fundamental%20programming.pdfhttp://staff.iiu.edu.my/azami/MATLAB%20tutorial%20of%20fundamental%20programming.pdfhttp://staff.iiu.edu.my/azami/MATLAB%20tutorial%20of%20fundamental%20programming.pdfhttp://staff.iiu.edu.my/azami/MATLAB%20tutorial%20of%20fundamental%20programming.pdf

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