JOURNAL OF COMPUTER SCIENCE AND ENGINEERING, VOLUME 13, ISSUE 1,
MAY 2012 9
Feature Extraction of an Iris for Pattern RecognitionSulochana
B. Sonkamble and Dr. Ravindra C. ThoolAbstract In this paper, we
propose a new approach for feature extraction of an iris. Feature
extraction is the most important step to improve the accuracy of
biometric- based person identification System. The
iris-pattern-based systems have recently shown very high accuracies
in verifying an individuals identity.This paper gives a new
approach to extract the region of interest of an iris and its
feature extraction.We have selected 100 iris images from CASIA
database and 100 images from our database which are captured from
50 different male and female volunteers with the help of the system
setup kept in our research laboratory. The proposed system consists
of five modules iris localization and segmentation method using
gradient vectors, normalization, feature extraction and matching.
The system uses the Canny Edge Detection Algorithm and Circular
Hough transform to detect the inner and outer boundary for
localization of an iris. The efficient iris segmentation technique
using gradient vector is to be used for extracting the iris region.
The extracted iris region can be normalizing into a rectangular
block of fixed dimensions. The Gabor wavelet transform techniques
have been applied on the data set to get feature vectors of an iris
which are to be used for the recognition. The extracted features
are stored in a vector contains the biometric information of an
individual. The Euclidian Distance method is proposed for comparing
the iris pattern. Two iris templates are to be used for testing.
The performance of the system can be increased by training the more
feature vectors of extracted iris images. Index TermsBiometric
Identification, Localization, Segmentation, Feature Extraction,
Pattern Recognition.
u
1 INTRODUCTION
N
OW a days the world is becoming more dependent on computer based
systems. Hence, computer security has been arisen to protect the
information using passwords. The security and the authentication of
individuals information are necessary for many different areas of
our lives, most of the people having their identity as ID Cards.
The ID and passwords can be stolen or forgotted. Biometric
identification provides a valid alternative to traditional
authentication mechanisms such as ID cards and passwords, whilst
overcoming many of the shortfalls of these methods; it is possible
to identify an individual based on who I am" rather than which
identity I possess". Biometrics is the only high confidential
method for recognition of a person using the features of the
individual instead of his or her knowledge like password or
belongings like ID card [1]. Biometrics has played an important
role over the recent years for developing the Security systems. The
Iris biometrics-based systems are proved the best among the all ;
fingerprint, palm, face and voice [3]. The iris recognition is
becoming a fundamental component of the computerized world with
various application areas in national ID card, banking, passport,
credit cards, smart cards, PIN, access control and network
security, etc.The iris-pattern-based systems have recently shown
very high accuracies in verifying an individuals identity.
Sulochana Balwant Sonkamble is with the Information Technology
Department at Marathwada Mitra Mandals, College of Engineering,
Pune, M.S., India. Dr.Ravindra C.Thool is with the Shri Guru Govin
Singhji Institute of Engineering and Technology, Nanded, M.S.,
India.
The human iris, located between the pupil and the sclera, has a
complex pattern. This iris pattern is unique to each person and to
each eye, and it remains stable over a person life time which is
observed through the clinical evidence. The body of this paper
details the steps of iris recognition including localization,
segmentation, normalization, feature extraction and classifier [4].
The performance of iris recognition system depends on the good
image quality and extremely clear iris texture details. We are
thankful to CASIA that they have provided the availability of
carefully designed iris image database of sufficient size for our
experiments. The CASIA Iris V3 contains a total of 22051 iris
images from more than 700 subjects and 1500 eyes. All iris images
are 8 bit graylevel JPEG files, collected under infrared
illumination. In our experiment we selected five different iris
images from CASIA-IrisV3-Interval which were captured by self
developed iris camera in two sessions, with at least one month
interval [2]. The figure 1 highlights the parts of iris image. In
this paper, we are presenting an efficient iris segmentation method
using gradient vector for the high confidence visual recognition of
a person. Section 2 describes the iris localization and
segmentation method using gradient vectors. Section 3 describes the
iris normalization process. In section 4 describes feature
extraction. In section 5 we are presenting the experimental
results. The section 6 highlights the conclusion and references and
biography are given at the end of the paper.
2012 JCSE www.journalcse.co.uk
10
point. For a specific curve f(x,a)=0, with parameter vector a,
form an array A(a), initially set to zero. This array is termed as
accumulator array [18]. For each pixel x, compute all a shown in
equation (2) and (3) and increment the corresponding accumulator
array entries by one. After each edge pixel x has been considered,
local maxima in the array A correspond to curves of f in the
image.
f ( x, a ) = 0df dx
(2)
( x, a ) = 0
(3)
Fig. 1. Sample Iris Image.
2 IRIS LOCALIZATION AND SEGMENTATIONThe main purpose of this
process is to locate the iris on the image and isolate the region
of interest from the rest of the eye image for further processing.
The iris image shown in figure 1, contains the parts such as pupil,
iris, sclera, eyelid etc [3]. So the captured iris image cannot be
used directly. The preprocessing is performed after the image
captured to isolate the region of interest. The iris region can be
divided into two circles, outer for iris-sclera boundary, another
inner for iris-pupil boundary [9] [10]. The eye images are used to
find the iris with precise localization of its boundaries using the
centre coordinates and radius of both the iris and pupil. The pupil
centre and iris centre are same and the radius can range from 0.1
to 0.9 of iris radius. The pupil circle must separate from the iris
to get the region of interest. For the image I(x,y) with centre
coordinates ( x o , y o ) and radius r, we can define any circle by
the equation (1). The Hough Transform is a standard computer vision
algorithm which can be used to find the shapes in the image. To
extract the features of the iris image, it is very essential to
find the simple shapes like straight lines, circles, ellipses etc.
In order to search the desired shapes in the image one must be able
to detect a group of pixels that are on a straight line or a smooth
curve. This can be achieved using the Hough transform [17]. The
circular Hough Transform can be applied to find the centre
coordinates ( x o , y o ) and radius r of the circular regions of
the pupil and iris as shown in the equation (1).2 2 xo + y o
= r
2
The edge characterizes the boundaries of the pupil and iris in
an image. The edges in image are the areas with high intensity
contrast from one pixel to next. There are many ways to perform the
edge detection. We are using the basic algorithm of gradient and
laplacian method. The gradient method detects the edges by
considering the maximum and minimum in the first derivative of the
image. The Laplacian method searches for zero crossings in the
second derivative of the image to find the edges. The searched edge
is having the one-dimensional shape of a ramp and calculates the
derivative of the image which can highlight its location. A
gradient -based algorithm is developed to overcome the limitations
to increase the accuracy of the iris image segmentation compared
with existing methods. The image segmentation using gradient vector
is a type of edge detection method called Gradient Method. First
applying the Gaussian Low pass Filter to the iris image to get
smoothed image with Sobel method. A pixel location with higher
intensity value is called as the edge if the value of gradient
reaches to some threshold. When first derivative is at maximum, the
second derivative is zero. Therefore, in second derivative, edge is
located at zero; this method is called as Laplacian. After that the
image is convolved with two 3X3 masks called horizontal and
vertical masks, which gives the horizontal and vertical gradient
values of the image in x (columns) and y (rows) direction
respectively [2]. The magnitude of the gradient values is
calculated using these horizontal and vertical values and finding
the square root of the combined values. Smooth the iris image with
a Gaussian Low pass Filter to reduce noise and unwanted data using
the equation (4). The equation (5) is used to calculate the value
of G .
(1)
The parameter space of the circular Hough Transform is three
dimensional ( xo , yo , r ) . Each point in image gives rise to a
locus of voting points in the 3D Hough space that will be a
surface. For a given radius r, the locus of possible circle centers
will be a itself circle of radius r and center ( x o , y o ) .
Therefore, in 3D space, the locus of possible parameter values can
be used to improve the efficiency of the edge strength in Hough
space. For the three parameters ( x0 , y0 ,r) arranged in such a
way that the resultant loci will pass through the same parameter
space. Therefore, many circles will intersect at a common
G ( x, y ) = G ( x, y ) f ( x, y )
(4)
Where,
G (1 =G
1 2
= ( 1
22
)e x y21
[( + ) / 2 ]2
2
)
e
[(
x
2
+
y
2
) / 2 ]
(5)
11
Fig. 2. (a) Original Image (b) Horizontal Edge map (c) Vertical
Edge Map (d) Edge Map caption.
Compute gradient of g(X, Y) using the gradient operator Sobel .
The Sobel operator performs a 2-D spatial gradient measurement on
an iris image. Then, the approximate absolute gradient magnitude at
each point is calculated. The Sobel operator uses a pair of 3x3
convolution masks, one to calculate the gradient in the x-direction
and the other to calculate the gradient in the y-direction [17] .
The gradient magnitude is calculated using the equation (7) and
direction is approximated using the formula given in equation (6)
the angle theta is calculated using the equation (8).
bined, forming a hierarchy of sub-regions. The neighboring
sub-regions with the smallest difference of average separating edge
gradient and average intensity are combined first, and the average
characteristics are recalculated for the new sub-region. For
specific application iris segmentation combination rule includes
function of edge scale, absolute region intensity, location within
the image, shape characteristics of the bounding edges [2]. The
iris images are collected from the CASIA (The Institute of
Automation, Chinese Academy of Science) database. The database
contains total of 22051 iris images from more than 700 subjects and
1500 eyes [2]. These images are used for segmentation and
implemented using the MATLAB function. The range of the values of
iris is from 90 to 150 pixels and pupil radius is from 28 to 75
pixels [9]. The circular Hough transform was performed within the
iris region. After this the six parameters are stored, x - y center
and iris radius, similarly x-y center and pupil radius. These
parameters are stored in a vector as the shape features of an iris
image. The top and bottom eyelids are isolated by using the linear
Hough. For isolating eyelashes a simple thresholding technique was
used as these are dark as compared to other region of iris. The
segmentation results are shown in figure 2, which gives the
successful segmentation of the maximum CASIA images.
3 IRIS NORMALIZATIONOnce, the iris is isilted from rest of the
eye region then transformed into a rectangle form to fixed
dimensions in order to allow comparision. It is necessary to
convert it radial to a polar form known as normalization. The
concentric iris image is unwrapped as shown in figure 44. Cartesian
to polar transform, known as normalization, is based on the
Daugmans rubber sheet model. Each point of the iris image is mapped
to a pair of polar coordinates (r, !), where radius r [0, 1] and
angle ! [0, 2"]. Regions with high occlusions are not considered,
and the amount of occlusion free areas can be used as quality
measure. The concentric iris image is unwrapped as shown in figure
4. The diameter of the pupil and the iris is not constant for all
images. It is necessary to transform these to normalize the
distance between pupil and iris by using the transform equations
given in (10) and (11). It is necessary to transform these to
normalize the distance between pupil and iris by using the
transform equations given in (10) and (11). The mapping of the
concentric iris region from (x , y) coordinates to thr normalized
polar representation is given by the equation (9).
G = Gx + G yM =2 2 Gx + G y
(6)
(7)
= tan 1 (
Gy Gx
)
(8)
The highest gradient value in the image falls on the edge. The
approach uses an edge detection algorithm to locate the edges in
the iris images. All resulting edges are linked to form a segmented
image and the region of interest is extracted. The edge detection
algorithm locates and follows edge segments, across the image and
across scales, using a large set of oriented Difference of Gaussian
(DoG) filters to estimate derivative directions and maxima. Maxima
are estimated by zero-crossings of second order derivative
estimates [18]. The derived edges are linked by extending dangling
edges in the direction of maximal gradient until an edge
intersection is reached. Canny edge detection is used to create an
edge map. The linear Hough transform is implemented to find the
inner and outer circles. The results of the gradient based
algorithm are shown in figure 2. Each elemental region enclosed by
edges is then characterized by the average value of the central
area, and the average gradient between adjacent regions. The
elemental regions are then iteratively com-
I ( x(r , ), y (r , )) I (r , )
(9)
Where I(x,y) is the iris image, (x,y) are the original Cartesian
coordinates, (r, ) are the corresponding polar coordinates. The
pupil coordinates are ( x p , y p ) and iris coordinates are ( xi ,
yi ) along direction.
12
w0 = u 02 + v 02
(13)
The iris pattern is convolved with the modulation and phase
Quantization of complex valued Gabor wavelet which is represented
by the equation (14). Figure 8 (a) shows the real component or even
symmetric filter characterized by a cosine modulated by a Gaussian
and 8 (b) shows imaginary component or odd symmetric filter
characterized by a sine modulated by a Gaussian [17].
h{Re ,Im } = sgn{Re ,Im } I ( , )e i (0 ) e ( r0 ) / e (0 ) /
dd
2
2
2
2
(14)Fig. 3. (a) Phase Quadrant Demodulation Code 2D Gabor
Wavelet (b) Real component and Imaginary component filter
characterized by a sine modulated by a Gaussian
x ( r , ) = (1 r ) x p ( ) + rx i ( )
(10) (11)
y (r , ) = (1 r ) y p ( ) + ry i ( )
The iris images are first scaled to get constant distance
between pupil and iris region. While comparing the two images one
is considered as the reference image. Once two images are same
dimension, the features are extracted from the iris region by
considering the intensity values along with the concentric circles
with origin at the center of pupil. The unwrapped iris region is
shown in figure 4. For normalization of the iris regions, center of
pupil is considered as reference point. The radial line pass
through the iris region is defined as angular resolution. The
normalization process creates the 2D array of horizontal dimensions
of angular resolution and vertical dimension of radial resolution.
The data points are chosen from the radial and angular position in
the normalized iris pattern. In case of matching of the two irises
these patterns are compared. The rectangular iris pattern is shown
in figure 4.
4 FEATURE EXTRACTIONOnce the normalized iris region is obtained
in the 2D rectangular form, this can be used to extract the
features. The iris pattern is demodulated to extract phase
information using the 2D wavelet. A 2D Gabor filter for any image
I(x,y) is given by the equation (12). The particular position in
the image is given by ( x 0 , y 0 ) , width and length is denoted
by ( , ) , the modulation is denoted by ( u 0 , v 0 ) The figure 3
shows Phase Quadrant Demodulation Code 2D Gabor Wavelet. This is s
generating complex valued coefficients whose real and imaginary
parts specify the coordinates of a pharos in the complex plane
[16]. Where the frequency w0 is given by the equation (13).G( x, y)
= e [( x x0 )2
Where h{ R , I } can be used as complex valued bit whose e m
real and imaginary parts are either 1 or 0 depending on the sign of
the 2D integral. The iris image in polar coordinate system is
denoted by I ( , ) . and are considered as the multi scale 2D
wavelet size parameters, is wavelet frequency, and ( r0 , 0 )
represent the polar coordinates of iris region. The phase
information is used for recognizing the iris whereas amplitude
information is used for finding the contrast and camera gain. After
feature extraction, an iris image is converted into feature
vectors. The template for iris is stored for matching. Two irises
can be compared using the features vectors. The difference of two
iris vector is calculated. The proposed matching algorithm,
Euclidian Distance can record results for iris images. The
comparison is performed intra-class database as well as inter-class
database. The results are shown in table 1. The encoded iris
pattern values generated from encoding process are given in each
filter recorded in table 3 with filter parameters frequency,
bandwidth, and multiplicative factor. The template is used for
comparison of different iris patterns. The normalization of the
iris region determines the radial and angular resolutions which are
to be used for encoding the iris pattern to create the iris
template.
5 EXPERIMENTAL RESULTSThe performance of iris recognition system
is tested at various stages. The tests were performed for image
preprocessing, segmentation, normalization, and feature extraction
and matching. The results of all testing are plotted and some of
those are shown in paper. The performance efficiency at each stage
is calculated and improved by minimizing the errors. The first and
most important step in iris recognition is the image acquisition as
recognition efficiency is depending on the quality the image. The
images are captured using the system set up with camera installed
in our College Research Laboratory. The collected data base is
having total 1000 images of right and left eye with very good image
quality and extremely clear iris texture details. These images are
collected from different 50 male and female
/ 2 +( y y0 )2 / 2 ]
e 2i[u0 ( x x0 )+v0 ( y y0 )] (12)
13
datasets were tested for intra-class and inter-class
comvolunteers. The images are captured by camera with high
resolution. The images were captured at constant distance TABLE
Table 1. The images used for segmentation from database Table 2.
Efficiency for Segmentation Results
Fig. 4. (a) Sample Normalized Iris and Encoded Iris Pattern(b)
Iris Image Capturing System setup.
Fig. 5. (a) Sample CASIA Iris Images. (b) Sample Iris Images
captured in our Research Laboratory.
.
and keeping constant natural lighting effect in our laboratory.
The images are color images that are processed to gray scale images
for further processing. Here, we have selected only 100 iris images
from CASIA database and 100 images from our captured images for
testing. The images from our database are selected which are giving
the good segmentation results. The images from CASIA database are
standard and selected based on good segmentation results. The iris
images from both the
parison given in table 1. Figure 5 shows some of the iris images
selected for preprocessing. Once the iris images are captured,
those images are preprocessed for iris localization. The image is
smoothed using the Sobel operated the horizontal and vertical
gradients are calculated. The pupil and iris boundaries are located
by applying the canny edge detection algorithm. The Hough transform
is used for segmenting the circular iris region. The details of the
segmentation are given above in the section 2. The images from the
CASIA as well as and the images acquired in our laboratory were
tested for segmentation. The results are given in table 1. The
segmentation efficiency for our acquired images is less due to the
various factors affecting for capturing the image like light,
distance between the camera and person. Figure 6 shows the
segmentation results for some of the iris images. After the
segmentation of an iris images, the extracted iris region is
normalized. The iris region is converted to polar as explained
above in section 3 to get the fixed sized rectangular iris region
to extract the features. The figure 6 shows some of the normalized
results of polar iris. The wavelet transform is applied on the
normalized iris region to encode, the iris pattern. The Gabor
wavelet is convolved with the iris pattern. The detail process of
feature extraction using wavelet is explained above in section 4.
Table 3 gives the some of the results of iris pattern of 20 iris
images.
6. CONCLUSIONThe paper Feature Extraction of an Iris for Pattern
Recognition is focused on feature extraction and encoding of the
iris pattern. The iris images near about 200, from CASIA database
and our own captured were tesvery good for CASIA images, ted for
intra-class and inter-class, comparison shown in the above section.
The
14
TABLE 3
Fig. 6.(a)Some of the Iris Segmentation Results. (b) Polar Iris
extracted for feature extraction.
segmentation efficiency for CASIA database is very good and for
our own captured can be inceased by capturing the images using the
high resolution camera. In this paper, the efficient and effective
methods are proposed for segmentation and feature extraction of an
iris image which gives effective and accurate results. The fixed
sized rectangular iris pattern was encoded to generate iris
template. The iris code was used for the comparison of inter-class
and intra-class iris pattern. The proposed system is found
successful and able to recognize a person using his/her iris
images. The experimental results show the better performance for
the proposed system.
Tab;e 3. Sample Feature Values on Multichannel Gabor Filtering ,
ACCV2002: The 5th Asian Conference on Computer Vision, 23-25
January 2002, Melbounce, Australia. Muhammad Khurram Khan, Jiashu
Zhang and Shi-Jinn Horng, An Effective Iris Recognition System for
Identification of Humans, IEEE 2004. Libor Masek, the University of
Western Australia, Recognition of Human Iris Patterns for Biometric
Identification, 2003. Yong Wang, Jiu-Qiang Han, Iris Recognition
using Independent Component Analysis, IEEE, 2005. Hugo Proenca and
Luis A. Alexandre, Portugal, A Metod for the Identification of
Inaccuracies in Pupil Segmentation , IEEE, 2006. Kresimir Delac,
Mislav Grgic, University of Zagreb, CROATIA, A Survey of Biometric
Recognition Methods, 46th International Symposium Electronics in
Marine, EELMAR-2004, 16-18 June 2004, Zadar, Croatia. The Institute
of Automation, Chinese Academy of Science, Note on CASIA-Iris V3 ,
October ,2008. Hugo Proenca and Luis A. Alexandre, ICIAP, UBIRIS :
A Noisy Iris Image Database , September, 2005. Shinyoung Lim,
Kwanyong Lee, Okhwan Byeon, Taiyun Kim, Efficient Iris Recognition
through Improvement of Feature Vector and Classifier , ETRI
Journal, 2001. John Daugman, How Iris Recognition Works, Invited
paper , IEEE, 2004. John Daugman,, High Confidence Recognition of
Person by Iris Pattern, University of Cambridge, The Computer
Laboratory, Cambridge CB2 3QG, UK, IEEE, 2001.
[6]
[7] [8] [9]
REFERENCES[1] Sulochana Sonkamble , Dr. Ravindra Thool, Balwant
A. Sonkamble, An Effective Machine-Vision System for Information
Security and Privacy using Iris Biometrics, WMSCI-2008, Orlando,
USA, ISBN 978-1-934272-31-2. Sulochana Sonkamble, Dr. Ravindra
Thool,Balwant SonkambleEfficient Iris Segmentation Methodology
Using Gradient Vector for the High Confidence Visual Recognition of
a Person, in IPCV,2009. Sulochana Sonkamble, Dr. Ravindra Thool,
Balwant Sonkamble, Survey of Biometric Recognition Systems and
Their Applications,Journal of Theoretical and Applied Information
Technology, ISSN: 19928645 , EISSN: 18173195,2010. Joseph Lewis,
University of Maryland, Bowie State University, Biometrics for
secure Identity Verification: Trends and Developments January 2002.
Lia Ma, Yunhong Wang, Tieniu Tan , Iris Recognition Based [10]
[2]
[11] [12] [13]
[3]
[14] [15]
[4]
[5]
15
[16] Book: Anil K. Jain, Patrick Flaynn, Arun A. Ross, Handbook
of Biometrics, Springer [17] Book: Rafael C. Gonzalez, Richard E.
Woods, Digital Image Processing, Addition-Wesley. [18] Book :
William K. Pratt, Digital Image Processing , WSE Wley.
Mrs.Sulochana Balwant Sonkamble received B.E. degree in Computer
Science and Engineering from Shri Guru Gobind Singhji Institute of
Engineering and Technology, Nanded, Mahrashtra state, India. in
1996, M.E. in 2002. She is pursuing Ph. D. in Computer Science and
Engineering from Shri Ramanand Teerth Marathwada University,
Nanded, and M.S. India. She is distinguished Assistant Professor in
Information Technology Department and presently working as Head of
Department of Information Technology at Marathwada Mitra Mandals
College of Engineering, Pune, Mahrashtra state, India. This author
became a Member of IEEE in 2006, is member of Computer Society of
India and life member of Indian Society for Technical Education.
The author have published two and/or presented paper in
International journal, five papers at national level and twelve
papers at international level. Author has got research grant from
Board of College and University Development , also sanctioned fund
for organizing state level and district level workshops from
University of Pune, M.S, India. Her research interest includes
computer vision, iris biometrics, image processing, neural network
and pattern recognition. Dr. Ravindra Thool received B.E. degree in
Electronics Engineering from Shri Guru Gobind Singhji Institute of
Engineering and Technology, Nanded, Mahrashtra state, India. in
1986, M.E. in 1991, and Ph. D. in Electronics and Computer Science
in 2003 from Shri Ramanand Teerth Marathwada University, Nanded,
and M.S. India. He is distinguished Professor and in Information
Technology Department since 1986 at Shri Guru Gobind Singhji
Institute of Engineering and Technology, Nanded, Mahrashtra state,
India and presently working as Head of Department . His research
interest includes computer vision, image processing, neural
networks and pattern recognition. He has published seven papers in
international journal twenty six papers in international
conferences.He is life member of Indian Society for Technical
Education and member of American Society for Agricultural
Engineering. He has been worked as Chairman Board of Studies in
Information Technology from and Member of Board of studies in
Computer science Engineering.