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Enhanced Feature Extraction Technique for Iris Template Generation

Sarika B. Solanke1, Ratnadeep R.Deshmukh2

Department Of Computer Science & Engg,Deogiri Institute Of Engg &Mgmt.Studies,

Dr.Babasaheb Ambedkar Marathwada University, Aurangabad, (M.S) India.

Department Of Computer Science & IT,

Dr.Babasaheb Ambedkar Marathwada University, Aurangabad,(M.S) India.

[email protected], [email protected]

Abstract

Abstract : In the process of iris recognition

localization and segmentation are the primary

preprocessing step that locate the iris and segments it

from the remaining part of the image. The

normalization and feature extraction are very

important and crucial stages which prepare the iris

image to extract the required unique features and

create the templates that can be compared to find the

uniqueness among the two irises. Normalization

allows transformation of iris region into a fixed size

dimensions that can be compared. In short

normalization produces constant dimension size

images of every localized input iris image which are

ready to further processing and comparison of such

images is possible due to their nature of constant

dimension size. In general the normalization process

prepares the segmented image for feature extraction

process. This paper discusses the enhanced

normalization process based on Daugman’s rubber

sheet model and feature extraction is based cumulative

sum based change analysis. Iris features are extracted

and the iris template is generated by horizontally and

vertically grouping the iris texture features as iris

codes.

Keywords : Iris Recognition ,Black hole search

mehod, Camus & Wilde’s Method, Daugman’s

Integro Differential operator, Rubber sheet model,

Cumulative Sum Based Change Analysis.

1. Introduction

Biometric identification is today’s need. Biometric

method identifies people based on physical or

behavioral characteristics. There are many techniques

for person identification. Iris recognition is the most

highly secured technique. There are various

applications of iris recognition system i.e. border

control in airports, access control in laboratories and

factories, identification for Automatic Teller

Machines , Educational Organization and police

verification rooms. Iris recognition system is given

mainly in following steps i.e Image Preprocessing,

Normalization, Feature Extraction, Template

matching[1,2,13,15,19,21,25].

1) Image Preprocessing: To find useful iris portion,

image preprocessing is necessary. Preprocessing is

given by using iris localization and segmentation

methods. Iris localization is used to detect the inner

and outer boundaries of iris image. Eyelids and

eyelashes are not required for preprocessing therefore

eyelids and eyelashes are detected and remove.

Segmentation is the process to partition a digital image

into sets of pixels. Segmentation of iris recognition is

used to locate objects and boundaries of an image.

2) Normalization: After segmentation of iris image, the

next step is the normalization. Normalization allows

transformation of iris region into a fixed size

dimensions that can be compared. In short

normalization produces constant dimension size

images of every localized input iris image which are

ready to further processing and comparison of such

images is possible due to their nature of constant

dimension size. In general the normalization process

prepares the segmented image for feature extraction

process.

3) Feature Extraction: Feature Extraction is the

important step in iris image processing.The matching

of iris recognition is done by using iris template.

Therefore it is necessary to extract iris pattern to create

a biometric template. The extracted features of the iris

are useful for correct recognition.

4) Template Matching: In the final step, the matching

will be done by using iris templates. This gives the

similarity between two iris templates. It gives a range

of values when comparing templates from the same

iris, and another range of values when comparing

templates from different irises is known as intra–class

comparisons and inter-class comparisons respectively.

Finally a decision is made to identify the person is

same or different.

Sarika B Solanke et al , International Journal of Computer Technology & Applications,Vol 8(4),499-506

IJCTA | July-August 2017 Available [email protected]

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mailto:[email protected]

mailto:[email protected]

2. Literature Survey

Zhenan Sun and etl… has proposed a combinational

classifier scheme by cascading the various

representations of matching algorithms in which

Global feature based classifiers are used instead of

classifiers based on local features to recognize noisy

iris signal. To measure the similarity and subpixel

accuracy between two intra class iris images a moment

based iris blob matching algorithm is developed.

Based on the experimental results, the authors suggest

that blob matching algorithm does not provide

promising results for iris recognition when used alone

whereas the use of cascaded classifiers introduces

extra computational cost but recognition accuracy of

system increases significantly [3].

Kazuyuki Miyazawa and etl… has implemented an

image matching technique in MATLAB for iris

recognition on images taken from CASIA Database.

The technique addresses the issues that are introduced

during the image acquisition due to the environmental

parameters such as spatial position, orientation, center

frequencies, size etc. These parameters have great

impact on the feature extraction process. To overcome

this problem phase components in 2D Discrete Fourier

Transforms (DFTs) of input iris images are used to

implement the phase based matching algorithm for iris

recognition system. In order to reduce the size of

registration iris data and to prevent the visibility of iris

images, they introduce the idea of 2D Fourier Phase

Code (2D FPC) for representing iris information. The

experimental results clearly demonstrate that 2D FPCs

are particularly useful for implementing iris

recognition devices using DSP technology. On the

other hand, the original phase-based iris recognition

algorithm is particularly suitable for implementing

high-accuracy iris verification/identification systems,

for which the recognition performance is a major

concern[6].

Neha Kak and etl… developed a network based iris

recognition system using the standard techniques like

canny edge detection, Hough transform algorithm,

Daugman’s algorithm and Backpropogation

algorithm. The authors have analyzed how the

network behaves when an input is given and for that

error rate specified was. The network has been trained

and tested for a number of eye images. The authors

stated two important observations related to

performance first, the increase in number of images

gradually decreases the learning rate of network and

second, the size of input states directly influences the

performance [7].

Shreyas Venugopalan and etl… has proposed a system

to bypass a filter based feature extraction techniques

that are based on iris bit codes using alternate iris

texture for feature extraction using spoofing. The

results show natural looking spoof iris images that

give a similar recognition (verification) performance

as a genuine iris of the same person [8].

Chung-Chih Tsai and etl… has proposed a robust and

effective matching technique that can be applied on

two sets of iris feature points for probabilistic fuzzy

matching using invariant iris properties. The proposed

matching algorithm is used to compute a similarity

score for two sets of feature points from a pair of iris

images. The experimental results show that the

performance of the system is better than those of the

systems based on the local features and is comparable

to those of the typical systems [9].

An alternative method for dimension reduction

scheme is implemented by Mahmoud Elgamal and

etl... In order to reduce the feature vector dimension

and increase the discriminative power, the principal

component analysis (PCA) has been used. k-NN

method classifies the objects in a feature space

depending upon the closeness of objects in training

example where instances with similar properties in

dataset exists in close proximity to each other. The

results shows that the proposed technique is robust

and effective compared with other recent works. The

recognition accuracy for pattern classification is

99.5%[10].

Chun-Wei Tan and etl… has achieved more stable

characterization of local iris features based on Zernike

moment-based phase encoding of iris features.These

features are computed from normalized image by

accommodating local pixel region variation through

partially overlapping regions. A joint strategy is

adopted to simultaneously extract and combine both

the global and localized iris features. The proposed iris

matching strategy tested using the iris images in three

publicly available databases: 1) UBIRIS.v2; 2) FRGC;

and 3) CASIA.v4distance. The results are compared

with the existing methods and prove to be effective

one. As compare to existing methods the results of

proposed method achieves higher rate of recognition

accuracy with an average improvement of 54.3%,

32.7%, and 42.6% in equal error rates, respectively,

for UBIRIS.v2, FRGC, and CASIA.v4-distance iris

databases [11].

Mohmmed A. M. and etl has proposed an iris

recognition system for nonideal iris images. To

segment the iris images captured under visible and

near infrared light two different alogorithms are

proposed. To segment the iris, combination of

expansion and shrinking is use on active contour

model that integrates new pressure force on the model.

Effective unwrapping is done on segmented iris using

non circular iris normalization method to obtain

normalized iris image. The proposed scheme is robust

in finding the exact iris boundary and isolating the

eyelids of the iris images. The proposed technique

achieve a high level of accuracy on the images taken

for testing and experimentation from the CASIA V4.0,



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MMU2, UBIRIS V1, and UBIRIS V2 iris databases

[12].

3. Formation of the Problem and

Methodology

Objectives :

1) To develop and perform efficient iris

localization algorithms for accurate and fast

iris segmentation.

2) To perform well feature extraction

algorithms for blinked eye or non-healthy

eye.

3) Development of enhanced feature extraction

techniques.

Contribution :

Research problem is based on iris recognition

biometric technique.In this research we are going to

find the efficient algorithms for performing

localization and feature extraction methods.We

perform our result with black hole search method for

localization and Cumulative Sums method for Feature

Extraction. We had used the standard MMU database

as data set to detect the iris of an eye. The template

generation results for MMU Database are achieved

with more than 99%. The proposed work is compared

with the Daugman’s Operator method. The proposed

method shows the efficient results as compared with

the other standard techniques. For non-healthy eye we

have a great solution in our proposed method. We are

detecting either upper half or lower half of the iris

through which we are generating the pattern. In this

method, the image of whole iris is not processed.

Either the lower half portion of upper half portion is

considered to generate the patterns. This helps in

solving the problem of change in color of image.

4. Localization and Segmentation of

Iris and Pupil in Region of Interest

Preparing input eye image for the preprocessing

involves localization and segmentation of iris. To

locate the region of interest(ROI) we use the black

hole search method and scale the image to a particular

region which has the maximum pixels that contains the

iris and unwanted pixel area is removed. To segment

the iris we use Camus & Wilde’s and Daugman’s

Integro Differential Operator

method[1,2,5,20,26,29,30]. The output of the

proposed method is shown in figure 1. The steps are

given in the algorithm. The proposed method is called

Iris Detection using Image Scaling (IDIS).

Figure 1A. Input image

Figure 1B. Region of Interest

Figure 1C. Segmentation of Iris

Figure 1: Localization and Segmentation

Algorithm 1:

Description: This algorithm takes ROI image as an

input for segmentation with minimum and maximum

values of iris radius and provides the segmented iris

image with pupil boundary and iris boundary.

Step 1: Start

Step 2: Read the Localized Image, rmin, rmax (rmin =

50, rmax=200)

Step 3: Scale all the parameters to the required scale

(scale=0.2)

Step 4: Convert the image to double as arithmetic

operations are not defined on uint8 images.

Step 5: Remove specular reflections if any using

morphological operation imfill.

Step 6: Generate a column vector of the image

elements that have been selected by thresholding; one

for x coordinate and another for y coordinate.

Step 7: Scans the neighbourhood of the selected pixel

to check if it is a local minimum and delete all pixels



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that are not local minima as they could not possibly the

centre coordinates.

Step 8: Re-compute the size after deleting unnecessary

elements

Step 9: Define two arrays maxb and maxrad to store

the maximum value of blur for each of the selected

centre points and the corresponding radius

Step 10: Perform coarse search using partial derivative

and line integral on the image.This method calculates

the partial derivative of the normalized line integral

holding the centre coordinates constant and then

smoothes it by a gaussian of appropriate sigma, rmin

and rmax are the minimum and maximum values of

radii expected. It also returns the maximum value of

blur and the corresponding radius along with finite

difference vector of blur.

Step 11: Make a fine search recursively repeating step

10 in search method.This method searches for the

centre of the pupil and its radius by scanning a 10*10

window around the iris centre for establishing the

pupil's centre and hence its radius.

Step 12: Draw the circle around iris and pupil using

approximation method for radius in polygon of n side.

Step 13: Display the segmented image containing

circles around iris and pupil.

Step 14: Stop.

5. Normalization

The following section describes the proposed

modified Daugman’s rubber sheet method for

normalization.

Daugman’s rubber sheet model

The Daugman’s rubber sheet model is widely used

method for normalization that unwraps the iris image

to convert it into a polar equivalent. To convert

Cartesian scale into polar scale pupil center is assumed

as the reference point. The figure below shows the

normalization around the center of iris and center of

pupil with radius r and angle θ.

Figure 2: Normalization process

The distance and angular position are the two

important factors that affect the iris images in

Cartesian coordinates. Illumination may cause non

linear iris patterns due to its impact on pupil size. To

compensate illumination variations, and impacts of

distance and angular positions a more accurate

normalization process is required. Daugman’s rubber

sheet [16,17] method is used to handle the unwanted

variations as discussed earlier. The unwrapping

parameters considered for Cartesian to polar

transformation are shown in figure 3 and given by

equation 1 and equation 2.

Figure 3: Unwrapping parameters in Rubber Sheet Model

………… 1

………… 1

Where,

In angular direction the process is inherently

dimensionless whereas the texture changes linearly in

radial direction. Hence the linear mapping of iris

texture is done onto the radial direction in the interval

of [0, 1] from border of pupil to the border of limbus

to create a dimensionless transformation in radial

direction. The figure 4A, 4B and 4C shows the original

segmented image converted into a ring and then

normalized to a fixed size of 100 X 300 pixels.

Figure 4A: Input Image



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Figure 4B. Iris ring

Figure 4C. Normalized imagefrom lower half of iris

Figure 4: Output of the Normalization process

In the proposed method we create a fixed dimension

sized 100 X 300 pixel normalized image for feature

extraction.In this case radial and angular resolutions

are set to 100 and 300 pixels respectively. The method

finds the equivalent position on polar axes for each

pixel in the iris. “interp2”function is used to

interpolate the normalized image into the size of

original image.The portion or pixels that yield a NaN

in normalized image are divided by the sum to get a

normalized value. Figure 5 shows the proposed

method for generating the template using the lower

half of the iris image for normalization and feature

extraction[20,22,23,27,30].

Figure 5: Proposed template generation method

6. Feature Extraction and Template

Generation

Most of the iris recognition system fails to correctly identify the unique iris only because of unreliable

feature extractions. Feature extraction has a great

impact of illumination and contrast. Thereby there is a

need to extract stable features that will improve the

efficiency of the recognition and matching process. In

most of the systems the iris features are obtained using

polar coordinates system, Gabor filters etc[18].

Daugman proposed Gabor’s complex 2D pass band

filter to extract texture from iris images is one of the

popular and widely used methods for feature

extraction. These methods involve calculation of

complex values by convolution and filtering at

different frequencies and positions in polar coordinate

system. There exists a dependency among the bits

extracted in iris codes due to the radial correlations

between different iris patterns. To overcome these

issues an enhanced feature extraction technique is

implemented using cumulative-sum-based change

analysis. The method uses the normalization image

block of 100 X 300 pixels which is generated through

normalization process[22,23,24,30].

Cumulative Sums for Feature Extraction

To calculate the cumulative sum the normalized image

is divided into number of cell regions where each cell

region contains 3 rows and 10 columns. Average of the

gray values is used a representative for calculation of

a basic cell region. Grouping of cell regions is done

horizontally and vertically as shown in figure 6. The

experimental results shows better results when

grouping of five cell region is done [4]. Then we

calculate the cumulative sum for every cell region

group which generates the iris feature code as shown

in figure 6.

Figure: 6 Division of normalized iris image into cell regions and grouping of cell regions

The calculation of cumulative sum is performed such

that X1, X2, …. And X5 are the five individual

representative values of cell region that belongs to first

group that is identified at the top left corner in figure 6

then



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X = (X1+ X2 +X3+X4+ X5) / 5 gives the

average of a particular selected cell region

group

Later on we compute the cumulative sum

from 0: S0=0.

Finally to calculate remaining cumulative

sums the difference between the current

value and average of previous sum is added

for all the five region cells that belong to the

group using following equation.

Si = Si-1+ (Xi –X) for i = 1, 2,….,5

…………(2)

To minimize the processing burden the cumulative

sums are computed using addition and subtractions for

feature extraction. The important steps in algorithm

for generating the iris code are given below. The for

loop is executed two times for horizontal grouping and

vertical grouping in which ‘i’ iterates over the rows in

steps of three and ‘j’ iterates over the columns in steps

of 10.

[maxg,p]=max(svalue(i,1:5)); % maxg is max value in

group, p is its index

[ming,q]=min(svalue(i,1:5)); % ming is min value in

group, q is its index

if(svaluev(i,j)>=svaluev(i,prev))%cell is on upward

slope then set iriscodeV to 2.

iriscodev(i,j)=255;

if(svaluev(i,j)<=svaluev(i,prev))

iriscodev(i,j)=128; %cell is on downward slope then

set iriscodeV to 1.

else

iriscodev(i,j)=0; %cell is not between minidx and

maxidx, set iriscodeV to 0.

Iris codes are generated using the analysis of

cumulative sums that illustrates iris pattern variations

in the values of gray pixels. The iris pattern changes

gray pixel values from dark to bright in an upward

slope of cumulative sum whereas in downward slope

it changes from bright to dark. Figure 7 shows the

example for generating iris code. If the cumulative

value of current group is greater than previous group

then the iris code is 2 as it is in upward slope. Similarly

the current group is in downward slope when the

cumulative sum is less than previous group’s

cumulative sum and iris code is set to 1. When the

cumulative sum is not in between maxidex and

minidex then iris code is set to 0 as shown in Fig. 7(a).

In Fig.7 (b), the second, third, and fourth cumulative

sums generate iris code 1 since they compose the

downward slope. Each cell has two iris codes: one for

the horizontal direction, the other for the

vertical[4,27,28].

Fig. 7. Example of iris code generation.

7. Results

The proposed method is implemented in MATLAB.

The proposed method of feature extraction works fine

for MMU V.1 iris database[14]. The template

generation results for MMU Database are achieved

with more than 99%.

Figure 8: Output of proposed method

8. Conclusion

The proposed method is more suitable to MMU V.1

iris database as compare to other iris databases. The

localization and segmentation based on black hole



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search method effectively separates the region of

interest from the input eye image. The normalization

method and feature extraction methods are quite less

time consuming and produces the desired results. In

future we propose to implement the real-time feature

extraction and template verification system that will

take two iris images and verify the uniqueness among

the input iris images. For verification a suitable

method will be identified.

ACKNOWLEDGMENTS

The authors would like to thank Computer Science &

IT Department, Dr.BAMU, Aurangabad for their

useful suggestions and providing the infrastructure to

complete the research work. This work is supported by

University Grants Commission.

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Sarika Solanke received Masters Degree in Computer

Science & Engg(MECSE) from MIT, Aurangabad.

She is pursuing Ph.D at

Dr.B.A.M.University,Aurangabad,(M.S.)India.

Currently She is working as Assistant Professor in

Computer Science & Engg.Department of DIEMS,

Aurangabad. She is a member of IETE and ICA. Her

research interests include Image Processing. Her

recent research is focused on Iris Recognition.

Dr.R.R.Deshmukh received the Ph.D Degree from

Dr.Babasaheb Ambedkar Marathwada

University,Aurangabad. He is currently working as

Head,DST-FIST Program Coordinator, Department of

CSIT,DR.BAMU,Aurangabad. He is Fellow &

Chairman,IETE Aurangabad Center,Life Member

ICA,CI,IEEE,IAEng,CSTA,IDES & SMACEEE.

Member of Management Council of

University.Visited University of Santiago Compostela

Spain in PEIN research Excellence

Program,HonKong,Shainghai-Chaina,Bankok-

Thailand,University of Washington Seattle,WA,USA.

He has authored and coauthored over 122 journals,

conference publications, including several book

chapters in the area.



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