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]
499
ISSN:2229-6093
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,
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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ISSN:2229-6093
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
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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ISSN:2229-6093
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
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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ISSN:2229-6093
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
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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ISSN:2229-6093
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
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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ISSN:2229-6093
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.
REFERENCES
[1] Wildes R., “Iris recognition: an emerging biometric
technology”, Proc. IEEE, 85: 1348-1363, September
1997.
[2] J.Daugman,“How iris recognition works”, IEEE
Transaction Circuit Systems VideoTechnology, 14(1),
21-30, 2004.
[3] Zhenan Sun, Yunhong Wang, Tieniu Tan, and Jiali
Cui,“Improving Iris Recognition Accuracy Via
Cascaded Classifiers”, IEEE TRANSACTIONS ON
SYSTEMS, MAN, AND CYBERNETICS—PART C:
APPLICATIONS AND REVIEWS, VOL. 35, NO. 3,
AUGUST 2005.
[4] J-Gook Ko,Youn-HeeGil,Jang-Hee Yoo,Kyo-IL
Chung,“A Novel and Efficient Feature Extraction
Method for Iris Recognition”,ETRI Journal, Volume
29, Number 3, June 2007.
[5] Richard Yew Fatt Ng Yong Haur Tay Kai Ming Mok,
“A Review of Iris Recognition Algorithms”,Computer
Vision and Intelligent Systems (CVIS) Group
Universiti Tunku , IEEE 2008.
[6] Kazuyuki Miyazawa, Koichi Ito, Takafumi Aoki, Koji
Kobayashi and Hiroshi Nakajima, “An
Implementation-Oriented Iris Recognition Algorithm
Using Phase-Based Image Matching”,International
Symposium on Intelligent Signal Procesing and
Communication Systems(ISPACS2006) Yonago
Convention Center,Toffori,Japan.
[7] Neha Kak, Rishi Gupta, Sanchit Mahajan,“Iris
Recognition System”, IJACSA) ,International Journal
of Advanced Computer Science and Applications,
Vol. 1,2010.
[8] Shreyas Venugopalan, and Marios Savvides, “How to
Generate Spoofed Irises From an Iris Code Template”,
IEEE TRANSACTIONS ON INFORMATION AND
SECURITY, VOL. 6, NO. 2, JUNE 2011.
[9] Chung-Chih Tsai, Heng-Yi Lin, Jinshiuh
Taur, and Chin-Wang Tao,“Iris Recognition
Using Possibilistic Fuzzy Matching on Local
Features”,IEEE TRANSACTIONS ON
SYSTEMS, MAN, AND CYBERNETICS—
PART B: CYBERNETICS, VOL. 42, NO. 1,
FEBRUARY 2012.
[10] Mahmoud Elgamal, Nasser Al-Biqami, “An
Efficient Feature Extraction Method for Iris
Recognition Based on Wavelet
Transformation”,International Journal of
Computer and Information Technology
(ISSN: 2279 – 0764) Volume 02– Issue 03,
May 2013 .
[11] Chun-Wei Tan, Student Member, IEEE, and
Ajay Kumar,Senior Member, IEEE,
“Accurate Iris Recognition at a Distance
Using Stabilized Iris Encoding and Zernike
Moments Phase Features”,IEEE
TRANSACTIONS ON IMAGE
PROCESSING,VOL.23,NO.9,
SEPTEMBER 2014.
[12] Mohammed A. M. Abdullah, Member, IEEE,
Satnam S. Dlay,Member, IEEE, Wai L. Woo,
Senior Member, IEEE, and Jonathon A.
Chambers, Fellow, “Robust Iris
Segmentation Method Based on a New
Active Contour Force With a Noncircular
Normalization”, IEEE TRANSACTIONS
ON SYSTEMS, MAN, AND
CYBERNETICS: SYSTEMS,2016.
[13] Sarika B.Solanke, Ratnadeep R.Deshmukh,
“Biometrics-Iris Recognition System”,a
study of promising approaches for secured
authentication, IEEE International
conference on “Computing for Sustainable
Global Development” ,BVICAM 2016.
[14] MMU1 database [online], available:
http://www.cs.princeton.edu/
∼andyz/irisrecognition.
[15] Sarika B.Solanke, Ratnadeep
R.Deshmukh,“The survey of Complications
in Iris Recognition”, International Journal of
Innovative Research in Science, Engineering
and Technology(IJIRSET), Volume 5 issue
10, October 16.
[16] H. Proenc¸a and L.A. Alexandre “Iris
segmentation methodology for non-
cooperative recognition” IEE Proc.-Vis.
Image Signal Process., Vol. 153, No. 2, April
2006.
[17] K Seetharaman, R Raghupathy “Iris
recognition for personal identification
system”. Elsevier Procedia Engineering 38 (
2012 ) 1531 – 1546
[18] Abikoye Oluwakemi C., Sadiku, J. S.,
Adewole Kayode S., and Jimoh Rasheed G.
“Iris Feature Extraction for Personal
Sarika B Solanke et al , International Journal of Computer Technology & Applications,Vol 8(4),499-506
IJCTA | July-August 2017 Available [email protected]
505
ISSN:2229-6093
Identification using Fast Wavelet
Transform”,International Journal of Applied
Information Systems (IJAIS) – ISSN : 2249-0868
Foundation of Computer Science FCS, New
York,USA Volume 6– No. 9, March 2014 –
www.ijais.org.
[19] Ankita Satish Adhau,D.K.Shedge, “Iris Feature
Extraction of a blinked eye”,International Journal of
Multidisciplinary Research and development,Vol.2
Issue 6,176-189,June 2015.
[20] Vanaja Roselin E. Chirchi and L.M.
Waghmare,“Feature Extraction and Pupil Detection
Algorithm Used for Iris Biometric Authentication
System”,International Journal of Signal Processing,
Image Processing and Pattern Recognition Vol.6, No.6
(2013), pp.141-160 ISSN: 2005-4254 IJSIP.
[21] Sonia Sangwan, Reena Rani , “A Review on: Iris
Recognition”,International Journal of Advanced
Research in Mechanical Engineering & Technology
(IJARMET) Vol. 1, Issue 1,2015.
[22] L. Ma, T. Tan, Y. Wang and D. Zhang, “Efficient Iris
Recognition by Characterizing Key Local Variations”,
IEEE Transactions on Image processing, Vol. 13, No.
6, 2004
[23] Adegoke,B.O,Omidiora, E.O,Ojo,J.A.Falohun S.,
“Iris feature Extraction: A survey”,Computer
Engineering And Intelligent
Systems,Vol.4,No.9,2013.
[24] Dolly Choudhary, Shamik Tiwari , Ajay Kumar Singh,
“A Survey: Feature Extraction Methods for Iris
Recognition”,International Journal of Electronics and
Computer Technology,IJECCT,Volume 2,Issue
6,November 2012.
[25] M.Sujithra,G.Padmavathi, “A Survey of Biometrics
Iris Recognition: Security,Techniques and Metrics”,
Asian Journal of Information Technology,2015.
[26] Zhaofeng He,Tieniu Tan ,Zhenan Sun,Xianchao
Qiu,”Toward Accurate and Fast Iris Segmentation for
Iris Biometrics”,IEEE TRANSACTIONS ON
PATTERN ANALYSIS AND MACHINE
INTELLIGENCE,VOL.31,NO.9 SEPTEMBER
2009.
[27] Ryan N.Rakvic,Bradley J.Ulis,Randy
P.Broussard,Robert W.Ives,Neil Steiner,
“Parallelizing Iris Recognition”, IEEE
TRANSACTIONS IN INFORMATION
FORENSICS AND
SECURITY,VOL.4,NO.4,DECEMBER 2009.
[28] Adams W.K.Kong,David Zhang Mahamed S.Kamel,
“An Analysis of IrisCode”, IEEE TRANSACTIONS
ON IMAGE
PROCESSING,VOL.19,NO.2,FEBRUARY 2010.
[29] Vineet Mumar,Abhijit Asati,Anu Gupta, “Iris
localization based on integro-differential operator for
unconstrained infrared iris images”,IEEE
International Conference on Signal
Processing,Computing and Control,2015.
[30] Samir Shah,Arun Ross,“Iris Segmentation
Using Geodesic Active Contours”,IEEE
TRANSACTION ON INFORMATION
FORENSIC AND
SECURITY,VOL.4,NO.4,DECEMBER
2009.
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.
Sarika B Solanke et al , International Journal of Computer Technology & Applications,Vol 8(4),499-506
IJCTA | July-August 2017 Available [email protected]
506
ISSN:2229-6093
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