Enhanced Approach to Iris Normalization Using Circular Distribution for Iris Recognition System

Enhanced Approach to Iris Normalization Using
Circular Distribution for Iris Recognition system
Taha Mohammad Hassan
Department of Computer Science,
College of Science, University of Diyala
Diyala, Iraq
dr.tahamh@sciences.uodiyala.edu.iq
Naji Mutar Sahib
Department of Computer Sciences,
College of Science, University of Diyala,
Diyala, Iraq
naji_e2006@yahoo.com
Abdul Salam Hassan Abbas
Department of Computer Science,
College of Science, University of Diyala
Diyala, Iraq
slamalqrtghwly@gmail.com
Abstract— As a part of a growing information society, security
and the authentication of individuals become nowadays more
than ever an asset of great significance in almost every field. Iris
recognition system provides identification and verification
automatically of an individual based on characteristics and
unique features in iris structure. Accurate iris recognition system
based on iris segmentation method and how localized the inner
and outer iris boundaries that can be damaged by irrelevant
parts such as eyelashes and eyelid, to achieve this aim, the
proposed method applied using [Iris Segmentation Based on
Brightness Correction ISBC] to edge detection then circle
distribution "CD" transformation on an eye image passed
through preprocessing operations. The proposed iris
normalization is done by using the important information that
resulted from iris segmentation such as center and radius of iris
to convert iris region in the original image from the Cartesian
coordinates (x, y) to the normalized polar coordinates (r, θ). The
proposed approach tested conducted on the iris CASIA (Chinese
Academy of Science and Institute of Automation) data set
(CASIA v1.0 and CASIA v4.0-interval ) iris image database and
the results indicated that proposed approach has 100% accuracy
rate with (CASIA v1.0), and has 100% accuracy rate with
(CASIA v4.0- interval) .
Keywords-Iris Normalization-CASIA(V1.0,V4.0-interval)
database-Circular Distribution (CD).
I. INTRODUCTION
The word ―biometrics is derived from the Greek words bio
(life) and metric (to measure) [1]. It is general term used
alternatively to describe a characteristic or a process. As a
characteristic it is measurable biological and behavioral
characteristic that can be used for automated recognition. As a
process it encompasses automated methods of recognizing an
individual based on measurable biological and behavioral
characteristic. Biometric identification may be preferred over
traditional methods (e.g. passwords, smart-cards) because its
information is virtually impossible to steal. Iris recognition has
a very good balance of all the properties. A number of
biometric characteristics are being used in various applications
as Universality, Uniqueness, Permanence, Measurability,
Performance, Acceptability, and Circumvention [2].
II. RELATED WORKS
Once the segmentation module has estimated the iris’s
boundary, the normalization module uses image registration
technique to transform the iris texture from cartesian to polar
coordinates. The process often called iris unwrapping, yields
a rectangular entity that is used for subsequent processing [3],
Iris Normalization has many algorithms such as : The John
Daugman, 2004 [4], has applied a Rubber Sheet Model to
map the sampled iris pixels from the Cartesian coordinates to
the normalized Polar coordinates. It transforms the iris region
from Cartesian coordinates (x, y) to Polar coordinates (r, θ)
where r lies in the interval of [0, 1] and θ is in the range of [0,
2π]. However, this algorithm does not compensate for the
rotation variance. I(x(r, θ ),y(r, θ))= I(r, θ ), where I(x, y) is
the iris image, (x, y) are the Cartesian coordinates and (r, θ )
are the polar coordinates.The Wildes, 1997 [5], has proposed
an image registration technique for normalizing iris textures.
In this method, a newly acquired image, Ia(u, v) would be
aligned with an image in the database, Id(u, v), that the
comparison is performed. The alignment process is a
transformation using a choice of mapping function, (U(x, y),
V (x, y)) such that for all (x, y), the image intensity value at
(x, y), in Ia is close to that at (x, y) in Id.
    
2
, , ,d ax y
I x y I x u y v dxdy    . (1)
The Li Ma, 2002 [6], 2000[7], approach for Iris Normalization
Li Ma [6] used Daugman’s Rubber Sheet Model and then Iris
Image Enhancement and De-noising. The homogeneous
rubber sheet model algorithm remaps each pixel in the
localized iris region from the Cartesian coordinates to polar
coordinates. Then Li Ma [6] maps the iris ring to a rectangular
block of the texture of a fixed size anti-clockwise. The
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
102 https://sites.google.com/site/ijcsis/
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normalized iris image still has low contrast and may have non-
uniform illumination (lighting) caused by the position of light
sources. Local histogram [7] analysis is applied to the
normalized iris image to reduce the effect of not uniform
illumination and obtain well-distributed texture image and
noise is removed by filtering the image with a low-pass
Gaussian filter. The Tisse [8], approach, the Rubber Sheet
Model is used which remaps each point within the iris region
to a pair of polar coordinates (p, θ) is used so the pupil
changes in size
X(p , θ) = (1-p) * XP(θ) + p *Xi(θ) (2)
Y(p , θ) = (1-p )* Yp(θ) + p *Yi(θ) (3)
Where Xp(θ), Yp(θ) and Xi(θ), Yi(θ) are the coordinates of the
pupillary and limbic boundaries in the direction θ. Boles [9]
proposed the normalization of images at the time of matching.
When two images are considered, one image is considered as a
reference image. The ratio of the maximum diameter of the
iris in this image to that of the other image is calculated. This
ratio is used to make the virtual circles on which data for
feature extraction is picked up. The dimensions of the irises in
the images are scaled to have the same constant diameter
regardless of the original size in the image.
III. OVERVIEW OF THE POPOSED APPROACH
The proposed approach for iris normalization consists of
three main stages: iris image acquisition, iris boundaries
localization, and iris normalization that is shown in Figure 1.
A. Iris Image Acquisition Stage
This step is one of the most significant and deciding
factors for obtaining a good result. A fine and clear image
eliminates the process of noise removal and also helps in
avoiding errors in calculation. In this case, computational
errors are avoided due to the absence of reflections, and
because the images have been taken from close proximity.
This project uses the image provided CASIA-Version 1.0 and
CASIA-interval Version 4.0.
B. Preprocessing on Eye Imageris Image Acquisition Stage
One of the major issues in the iris segmentation and
recognition system is the preprocessing, which can result in
accurate iris and pupil localization and thereby leads to
excellent iris recognition system performance. In this work,
the preprocessing on image consist of the four stages: convert
the color image (if the image is color) to the grayscale image,
brightness correction, convert to binary image, and enhanced
by the median filter to remove some noise around pupil as
shown in Figure. 2.
Image acquisition
Preprocessing
Gray-Scale
image
Brightness
correction
Binary image
Smoothing
image
Pupil Detection
Many objects
detection in
binary image
Choice a
largest object
as pupil
Iris Outer Boundary Detection
Split
operation
Enhancement operation
Enhance with X-direction
Enhance with Y-direction
Enhance with Median filter
Iris
detection
Iris
isolation
Iris Normalization
Translate with
Circular
Distrebution
Translate with
Circular
Distrebution
Translate with Circular
distribution
Resize Iris Region
Figure. 1: Block diagram of the proposed iris normalization using circular distribution.
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Figure. 2. Preprocessing stages: (a) The input image, (b) Apply the
brightness correction,(c)Apply the thresholding method (binary
image ), (d) Removing noise in the image using (median filter)
operation.
In brightness correction two brightness values are
choosing automatically depending on the average (mean) of
the input (original) image:
A - The first brightness (FB) value is an addition to the
“grayscale image” - in most cases worth 20, except for some
images that have eyelashes and eyelids very close to the pupil
and are more than 20.
B - The second brightness (SB) value is an addition to the
“original image” most cases are worth 20, except for some
images that are high brightness and are less than 20 or do not
need to add brightness.
Where the best two brightness (FB and SB) choosing to
depend on:
1) Average of a grayscale image from 150 to 159 the best
two brightness is [FB=20, SB=20].
Average of a grayscale image from 190 and more above in
these cases do not need to add any brightness correction.
C. Pupil Detection
In this paper, the inner boundary (pupil-iris boundary) is
detected before the outer boundary (iris-sclera boundary), due
to the fact that the pupil region is the darkest region in the eye
image and can be detected easily. In addition, this can
contribute to improving the accuracy and the speed of
detecting the outer boundary as will be explained later.
The pupil localization is a process to localize the pupil by
transforming the gray scale eye image into a binary image
using the threshold method depended on the stable threshold is
126. In this method, all pixels that have values Greater than
the threshold are marked as edge points. In this method, there
could exist some noise present in the binary image, due to
other dark regions, such as eyelashes and eyelids. A filtering
operation (median filter) applied to eliminate such noise.
The pupil region is almost completely detected in the
binary image after enhanced (median filter) image, then detect
many objects contain true value as shown in Fig 3(b). After
this operation, using a set of layers, each object detection that
is putting in one of the layers as shown in Fig 3(c), and then
apply correction on the values of each layer as shown in Fig
3(d).After many object detection and correction in the image,
drawing each object finding, as shown in Fig 3(e). Then
choose the large box as pupil object. Since it can be modeled
as a box shape, the pupil is detected correctly by employing a
modified to obtain the center coordinates and radius of the
pupil box, as shown in Fig 3(f).
a b
c d
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T F T T
T T T T
T T F F
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T T F F
T F F F
T F F F
T F F F
T T T T
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T T T T
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F F T F F
F T T T F
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(a)
(b)
(c)(d)
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Figure. 3. Pupil localization: (a) Enhanced binary image, (b)
Many object detection, (c) Many layers using (each object put in the
layer ), (d) Correct the value of objects in the layer, (e) drawing the
objects, and (f)Choice large object as the pupil.
Fig. 4-5, shows the correct detection of the inner
boundary of the iris-pupil region, On CASIA version 1.0 and
version 4.0-interval respectively.
Fig. 4. Pupil localization stages in the CASIA version 1.0: (a) The
input image, (b) Applying the increasing brightness of the image (c)
Applying the thresholding method, (d) The output of the filtering
operation (e) Detect many object, (f)Choice large object as the pupil.
Fig. 5. Pupil localization stages in the CASIA version 4.0 interval: (a)
The input image, (b) Applying the increasing brightness of the image
(c) Applying the thresholding method, (d) The output of the filtering
operation (e) Detect many object, (f)Choice large object in the pupil.
D. Iris Outer Boundary Detection
This stage is to find the outer iris boundary. it aims to find
iris region, which is characterized as a color region that is
surrounded by white color area (Sclera) from outside and
black color area (pupil) from inside. This stage should be
accomplished with high accuracy because it significantly
affects the success of the system. To make the process simple
and accurate. The proposed algorithm of iris outer boundary
localization that includes six steps: split operation of an image,
enhancing by X-direction, enhancing by Y-direction, applying
a median filter, iris localization and fitting circle, and iris
isolation as described in the Fig 6-7, on CASIA version 1.0
and version 4.0-interval respectively.
The split operation: applied to find iris outer
boundary separately, this operations calculates the average
value (Avg) of the points extended along the inclined line in
the “Gray Scale Image enhanced by brightness correction”,
and then compare each point (P) along the inclined line with
average value (Avg): if the value of (P) is below Avg then set
(P) value to zero, else set (P) value to one, Fig 6-7(c).
The Enhancing image with X – direction: this
operation to remove the small gaps or pores (i.e., short runs of
zeros or ones) which may found in the binary sequence of (P)
in Y-direction; this step will lead to long run of zeros followed
by long run of ones, Fig 8-9(d).
The Enhancing image with Y – direction: this
operation to remove the small gaps or pores (i.e., short runs of
zeros or ones) which may found in the binary sequence of (P)
in Y-direction; this step will lead to long run of zeros followed
by long run of ones, Fig 6-7(e).
Median filter operation: applied to an enhanced
images by Y- direction pass through the steps above and not
on an original image directly so that to reduce the number of
undesired edges in original images, as a result, this will
increase the accuracy of edge detection, Fig 6-7(f).
Iris detection: after median filter on the image, Search
for transition point (from zero to one) along the inclined scan
line; this point will be considered as a boundary point between
iris and sclera region, Fig 9-10(g).Iris isolation: this operation
used to isolate the iris from eye image, Fig 6-7(h).
a b
c d
e f
a b
c d
e f
a b
c d
e f
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Figure. 6. Iris outer boundary localization stage in CASIA version 1.0
: (a)The original image , (b) brightness correction , (c) Applying the
split operation on brightness correction image, (d) Applying the
enhancement on X-direction on split operation image (e) Applying
the enhancement on Y-direction on enhanced image with X-
direction, (f) Applying the enhancement using median filter on
enhanced image with Y-direction, (g) applying the circle on the
enhancing image with median filter,(h)output of the localized iris
boundary.
Figure. 7. Iris outer boundary localization stage in CASIA version 4.0
interval: (a)The original image , (b) brightness correction , (c)
Applying the split operation on brightness correction image, (d)
Applying the enhancement on X-direction on split operation image
(e) Applying the enhancement on Y-direction on enhanced image
with X-direction, (f) Applying the enhancement using median filter
on enhanced image with Y-direction, (g) applying the circle on the
enhancing image with median filter ,(h)output of the localized iris
boundary.
E. Iris Normalizatoin
Normalization refers to preparing a segmented iris
image for the feature extraction stage. The inner and outer iris
boundaries are located in an original image and can get iris
region directly from an image.
1- Polar Transform using Circular distribution:-
Anytime there is an opportunity for the center line of
a part or component to be displaced in two directions from a
target position the circular normal distribution may govern the
behavior of the displacement [10].
An example of a problem involving circular normal
data is shown in Fig. 8(a). The Figure shows the (x, y)
positions of parts and their intended target at the origin of the
(x, y) coordinate system. Individually the distributions of x
and y are normal, but it is the combined effect of x and y that
is of concern. The relevant displacement of a part from its
intended position is measured by the radial distance r taken
relative to (x, y) = (0, 0). The value of r for a part depends on
its x and y displacements according to:
r = (4)
It is the distribution of r that is circular normal. In
some cases (x, y) data may be available and in others, only the
r values might be measured. A typical question to be
addressed that involves the circular normal distribution for
Fig.8(b), would be to determine an upper spec limit for r such
that a species fraction of the observations meets the spec [11].
To calculate points along the circular boundary using polar
coordinates r and 8(angle). Expressing the circle equation in
parametric polar form yields the pair of equations
(5)
(6)
When a display is generated with these equations
using a fixed angular step size, a circle is plotted with equally
spaced points along the circumference. Larger angular
separations along the circumference can be connected with
straight line segments to approximate the circular path [11].
Fig.8: (a) proposed polar transform procedure, (b) Iris
region in Polar Coordinate, (c) Resize iris region.
a b
c d
g h
e f
g h
b c
a
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Algorithm 1:- The Proposed Polar
Transform using Circular distribution
Goal: will transform iris region in the original image from the
Cartesian coordinates to the polar coordinates.
Input: Original Image Q(x, y), radius, (x, y).
Output: Iris Region in Polar Coordinate [datafeat] .
Process:
Step 1: Let data set is array [360, Maxradius]
Step 2 :
For all (i , j = 0; i < 360, j <= Maxradius - 1; i = i + 0.1,j=j+1 )
Set angle i * PI / 180;
Set y xc + j * Cos(angle);
Set x yc + j * Sin(angle);
Get x-value = Original Image Q(x , y)
Set datafeat x-value
2- Iris Region Resize
The second step in iris normalization stage is iris
region resize which uniform all iris region images in the
system to be imaged in a fixed size. Because the iris is
captured in different sizes for different people, as well as for
the same person. This is due to several factors (e.g., a variation
of illumination, change in distance between the camera and the
eye, eye position, rotation of the camera, head tilt and rotation
of the eye within the eye socket) , as shown in the Fig.8(c).
IV. RESULT AND DISCUSSION
The proposed iris segmentation approach tested on the
iris databases (CASIA v1.0 and CASIA v4.0 interval). The eye
image inputted into iris recognition system and pass through
all stages as described in the following stages:
Stage1: select eye image from the database mentioned above
as shown figure 9, to pass through the iris boundary
localization steps.
Figure. 9. Original eye images: (a) CASIA v1.0, (b) CASIA
v4.0-interval.
Stage2: The results of pupil and iris boundaries
localization steps in Figure. 10-11, on CASIA V 1.0 and
CASIA V 4.0 interval respectively.
Figure. 10. Pupil localization stages: (a) On CASIA version 1.0 and
(b) On CASIA version 4.0 interval.
Figure. 11. Iris outer boundary detection stages: (a) On CASIA
version 1.0 and (b) On CASIA version 4.0 interval.
Stage3: Iris normalization.
The iris normalization result, transformation of iris region to
Polar Coordinate in a fixed size.
1- The Iris Data Base CASIA version 1.0: as shown in
Fig.12.
Figure.12.Iris normalization results in CASIA V1.0 : (a) The
iris after isolation, (b) Polar transform with circular
distribution, (c) Iris image resize.
a b
a
b
a
b
a b c
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2- The Iris Data Base CASIA version 4.0-interval: as
shown in Figure.13.
Figure.13.Iris normalization results in CASIA V4.0-interval:
(a) The iris after isolation, (b) Polar transform with circular
distribution, (c) Iris image resize.
CONCLUION
In this paper, a new approach to iris normalization is
presented, which focused on iris segmentation and
normalization from eye image. The idea of this paper is based
on finding iris boundaries of the accurate method using edge
detector and circular distribution transform, this stage is very
critical and effects on iris recognition system results. In
addition, the proposed iris normalization stage divided into
two parts iris polar transform and iris region resize to increase
the accuracy and speed. Experimental results show that this
approach has a good iris normalized performance. The
proposed approach has implemented in c # language and
Microsoft access office 2013 run under Microsoft Windows 10
Home Premium Operating System Version 6.1.7601 Service
Pack 1 Build 7601. The proposed approach has an average
high accuracy.
Table 1: Calculation of Average Accuracy
Data
Base
No. of
Image
No. Correct
Segmentation
No. Faulty
segmentation
Average
Accuracy
CASIA
-V1.0 756 756 0 100%
CASIA
–V4.0
interval
1100 1100 0 100%
REFERENCES
[1] Bhalchandra, A., Deshpande, N., Pantawane, N. and Kharwandikar, P.
(2008), ―Iris Recognition‖, Proceedings Of World Academy Of
Science, Engineering And Technology, Vol. 36, pp. 1073-1078.
[2] Jain, A., Flynn, P., and Ross, A. (2008), ―Handbook of Biometrics‖,
Michigan State University, USA, Flynn University of Notre Dame,
USA, West Virginia University, USA, pp. 79-98.
[3] Nithyanandam.S , Gayathri.K.S , Priyadarshini P.L.K , "A New IRIS
Normalization Process For Recognition System With Cryptographic
Techniques", CSE Department, PRIST University, International Journal
of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011, ,
Thanjavur, Tamilnadu,India.
[4] J. Daugman. “How iris recognition works”, IEEE Trans. CSVT, vol. 14,
no. 1, pp. 21 – 30, 2004.
[5] R.P. Wildes. “Iris Recognition: An Emerging Biometric Technology”,
Proceedings of the IEEE, vol. 85, pp. 1348-1363, 1997.
[6] Li Ma, Y. Wang, and T. Tan. “Iris recognition using circular symmetric
filters”, International Conference on Pattern Recognition, vol. 2, pp.
414-417, 2002.
[7] Y. Zhu, T. Tan, and Y. Wang. “Biometric Personal Identification Based
on Iris Patterns”, Proceedings of the 15th International Conference on
Pattern Recognition, vol. 2, pp. 2801-2804, 2000.
[8] C. Tisse, L. Martin, L. Torres, and M. Robert.. “Person identification
technique using human iris recognition”, International Conference on
Vision Interface.
[9] W. Boles and B. Boashash, "A Human Identification Technique Using
Images of the Iris and Wavelet Transform," IEEE Trans. Signal
Processing, vol. 46, pp. 1185-1188, 1998.
[10] http://en.wikipedia.org/wiki/Circular_distribution
[11] Paul Mathews, Mathews Malnar, and Bailey, in ” The Circular
Normal Distribution”, Proceedings of the IEEE 87, 2000.
a b c
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Enhanced Approach to Iris Normalization Using Circular Distribution for Iris Recognition System

Enhanced Approach to Iris Normalization Using Circular Distribution for Iris Recognition System

Enhanced Approach to Iris Normalization Using Circular Distribution for Iris Recognition System

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