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Application of Geodesic Active Contours in iris Segmentation
Kapil Rathor1
1 Assistant professor, EXTC, St. John College of Engineering and
Technology, Palghar, Maharashtra, India
----------------------------------------------------------------***--------------------------------------------------------------
Abstract A biometric system provides
automatic identification of an individual based
on a unique feature or characteristic possessed
by the individual. Iris recognition is the most
reliable and accurate biometric identification
system. The richness and apparent stability of the
iris texture make it a robust biometric trait for
personal authentication. The performance of an
automated iris recognition system is affected by
the accuracy of the segmentation process used to
localize the iris. Most iris recognition systems
consist of an automatic segmentation system that
is based on the Hough transform. These systems
localize the circular iris and pupil region.
However, it is difficult to segment iris images
acquired under nonideal conditions using such
conic models. In this paper, a novel iris
segmentation scheme employing geodesic active
contours (GACs) to extract the iris from the
surrounding structures is described. The
proposed scheme elicits the iris texture in an
iterative fashion and is guided by both local and
global properties of the image.
Index Terms Geodesic active contours
(GACs), iris codes, iris recognition, iris
segmentation, level sets.
1. INTRODUCTION
With increase in terrorism and illegal acts, there is a growing
demand for more secure and reliable identification in our society
that can replace the traditional means of identification. Biometric
technologies, based on recognition of humans based on behavioral or
physiological characteristics, promises to be an effective
solution. Biometric recognition can be described as automated
methods to accurately recognize individuals based on distinguishing
physiological and/or behavioral traits. It is a subset of the
broader field of the science of human identification. Biometrics
offers the means to identify individuals without requiring that
they carry ID cards and badges or memorize passwords. Examples of
biometric technologies
include fingerprint recognition, face recognition, iris
recognition and many others. The parts of body and behavior have
been used for years as a means of person recognition and
authentication. For example, fingerprint has been used for a long
time in security and access applications. In comparison to other
biometric features such as face, fingerprint, retina, and hand
geometry, iris is seen as a highly reliable biometric technology
because of its stability, and high degree of variation between
individuals. The iris is seen as a highly reliable and accurate
biometric technology because each human being is characterized by
unique irises that remain relatively stable over the life period.
Iris is present in the form of ring around pupil of a human eye in
all the human beings. Its complex pattern contains many distinctive
features such as arching ligaments, crypts, radial furrows, pigment
frill, Pupillary area, ciliary area, rings, corona, freckles and
zigzag collarette [1][2] which gives a unique set of feature for
each human being, even irises of identical twins are different.
Surface of the iris is composed of two regions, the central
Pupillary zone and the outer ciliary zone. The collarette is the
border between these two regions. The collarette region is less
sensitive to the pupil dilation and usually unaffected by the
eyelashes and eyelids. [3]
1.1 Features of the human iris Now some of the visible features
of the human iris will be described, which are important to
identify a person, especially pigment related features, features
controlling the size of the pupil, visible rare anomalies, pupil,
pigment frill and collarette. The crypts, in the figure 1 shown as
number 5, are the areas in which the iris is relatively thin. They
have very dark colour due to dark colour of the posterior layer.
They appear near the collarette, or on the periphery of the iris.
They look like sharply demarcated excavations. The pigment spots,
in the figure 1 shown as number 6, are random concentrations of
pigment cells in the visible surface of the iris and generally
appear in the ciliary area. They are known as moles and freckles
with nearly black colour.
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Features controlling the size of the pupil are radial and
concentric furrows. They are called contraction furrows and control
the size of the pupil. Extending radically in relation to the
center of the pupil are radial furrows. The typical radial furrows
may begin near the pupil and extend through the collarette. The
radial furrows are creased in the anterior layer of the iris, from
which loose tissue may bulge outward and this is what permits the
iris to change the size of the pupil. The concentric furrows are
generally circular and concentric with the pupil. They typically
appear in the ciliary area, near the periphery of the iris and
permit to bulge the loose tissue outward in different direction
than the radial furrows.
Fig- 1: Features controlling the size of the pupil (1-pigment
frill, 2-pupillary area, 3-collarette, 4-ciliary
area, 5-crypts, 6-pigment spot) [4]
Collarette is the boundary between the ciliary area and the
pupillary area. It is a sinuous line shown as number 3 in Fig 1,
which forms an elevated ridge running parallel with the margin of
the pupil. The collarette is the thickest part of the human iris.
The human iris may have some of the rare anomalous visible
features. Due to aging or trauma, atrophic areas may appear on the
iris, resulting in a "moth-eaten" texture. Tumours may grow on the
iris, or congenital filaments may occur connecting the iris to the
lens of the eye. [4]
2. IRIS LOCALIZATION To find out the three boundaries for iris
localization, first, find the inner boundary between the pupil and
the iris. Second, find the outer boundary between the iris and the
sclera. For finding these boundaries, an edge detector and some
image processing methods are applied to an eye image. [5]
2.1 Inner Boundary
Though the pupil area has a low gray level and looks dark in the
eye image, it can be found by edge detector. In addition,
characteristics of the pupil remove some of the unnecessary areas
and help to find the inner boundary. To find out the inner
boundary, Canny edge detector is applied to the eye image after
excluding unnecessary areas. [5]
2.2 Exclusion on Unnecessary Areas
The pupil belongs to the dark side and the noise of the
reflection off glasses belongs to the light side in the each image.
Therefore, it is possible to remove the light side which has higher
gray level. The mean value represents the boundary between the
light and dark side, so it can adjust gray levels which have lower
than mean value, to the whole gray level [6].
2.3 Edge image of eye by canny edge detection algorithm
The purpose of edge detection in general is to significantly
reduce the amount of data in an image, while preserving the
structural properties to be used for further image processing.
Before applying edge detection algorithm the image is filtered for
removing noise contains from the image after that the canny edge
detection algorithm is applied to the image.
2.3.1 Smoothing In order to filter out any noise in the original
image before trying to locate and detect any edges Gaussian filter
can be used. As the Gaussian filter can be computed using a simple
mask, it is used exclusively in the Canny algorithm. Once a
suitable mask has been calculated, the Gaussian smoothing can be
performed using standard convolution methods. A convolution mask is
usually much smaller than the actual image. As a result, the mask
is slid over the image, manipulating a square of pixels at a time.
The larger the width of the Gaussian mask, the lower is the
detector's sensitivity to noise. The localization error in the
detected edges also increases slightly as the Gaussian width is
increased. [7]
2.3.2 Edge Detection The Canny algorithm basically finds edges
where the gray scale intensity of the image changes the most. These
areas are found by determining gradients of the image. Gradients at
each pixel in the smoothed image are determined by applying what is
known as the Sobel-operator. [8]
2.3.3 Outer Boundary
It is very difficult to locate the boundary between the iris and
the sclera when it is blurred. To find out the outer boundary,
canny edge detector is applied to the original eye image again.
After that the circular Hough transform is used to find the centre
and radius of the iris. Completing the process of Hough transform
the Hough accumulator contains the values of number of circle
passing to the particular point. So the point from which
maximum
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circles are passing is the centre of the iris and corresponding
radius is the radius of the iris. [5]
3. IRIS SEGMENTATION USING GACS (GEODESIC ACTIVE CONTOURS)
The iris localization procedure by using GACs can be broadly
divided into two stages: A. Pupil segmentation and B. Iris
segmentation.
3.1 Pupil Segmentation
To detect the pupillary boundary, the eye image is first
smoothed using a 2-D median filter and the minimum pixel value is
determined. The iris is then binarized using a threshold value.
Fig. 2(b) shows an iris image after binarization.
Fig- 2: Pupil binarization. (a) Image of an eye with dark
eyelashes. (b) Threshold binary iris image. [9] As expected, apart
from the pupil, other dark regions of the eye (e.g., eyelashes)
fall below this threshold value. A 2-D median filter is then
applied on the binary image to discard the relatively smaller
regions associated with the eyelashes. This reduces the number of
candidate iris pixels detected as a consequence of thresholding as
seen in Fig. 3(a). Based on the median-filtered binary image, the
exterior boundaries of all the remaining objects are traced as
shown in Fig. 3(b). Generally, the largest boundary of the
remaining regions of the eye corresponds to the pupil.
Fig-3: Pupil Segmentation. (a) 2-D Median filtered binary iris
image. (b) Traced boundaries of all the remaining objects in the
binary image (shown in gray color). (c) Fitting circle on all
potential regions where the pupil might be present (shown in gray).
[9]
3.2 Iris Segmentation
To detect the limbic boundary of the iris, a novel scheme based
on a level sets representation [10][11] of the GAC model is
employed. This approach is based on the relation between active
contours and the computation of geodesics (minimal length curves).
[12] The technique is to evolve the contour from inside the iris
under the influence of geometric measures of the iris image. GACs
combine the energy minimization approach of the classical snakes
and the geometric active contours based on curve evolution.
3.2.1 GACs (Geodesic Active Contours) Let (t) be the curve, that
has to gravitate toward the boundary of any object, at a particular
time as shown in Fig. 4. The time corresponds to the iteration
number. Let be a function defined as a signed distance function
from the curve (t). Thus, distance of point to the curve (t) . (x ,
y) is signed distance of point (x , y) from the nearest point in
the curve (t) . (x , y) =
0 , if x, y is on the corve
< 0, if x, y is inside the curve
> 0, if x, y is outside the curve
..... (1)
is of the same dimension as that of the image that is to be
segmented. The curve (t) is a level set of the function . Level
sets are the set of all points in where some constant. Thus, = 0 is
the zeroth level set, = 1 is the first level set and so on. is the
implicit representation of the curve (t) and is called as the
embedding function since it embeds the evolution of (t). The
embedding function evolves under the influence of image gradients
and regions characteristics so that the curve approaches the
boundary of the object. Thus, instead of evolving the parametric
curve the embedding function itself is evolved. In our algorithm,
the initial curve is assumed to be a circle of radius just beyond
the pipullary boundary.
Fig-4: Curve evolving towards the boundary of the object.
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Let the curve be the zeroth-level set of the embedding function.
This implies that
d
dt= 0
By chain rule d
dt=
d
dx
dx
dt+
d
dy
dy
dt+
d
dt
i.e d
dt= . (t)
Splitting t the in the normal (N(t)) and tangential (T(t))
directions,
= . ( . + . )
Now since is perpendicular to the tangent to ()
= . ( . (2)
The normal component is given by
=
| |
Substituting this in (2)
= | |
Let be a function of the curvature of the curve k, stopping
function K (to stop the evolution of the curve) and the inflation
force (to evolve the curve in the outward direction) such that
= (
+ ). | |
Thus, the evolution equation for such that remains the zeroth
level set is given by
= + + . (3)
Where K, the stopping term for the evolution, is an image
dependant force and is used to decelerate the evolution near the
boundaries; is the velocity of the evolution; indicates the degree
of smoothness of the level sets; and k is the curvature of the
level sets computed as
k = y
22+ x2
(x2+y
2)32
where is the gradient of the image in the x direction, is the
gradient in the y direction, is
the second-order gradient in the x direction, is
the second-order gradient in the y direction and
is the second-order gradient, first in the direction and then in
the direction. Equation (3) is the level set representation of the
GAC model. This means that the level-set C of is evolving according
to
= + (. ) (4)
Where is the normal to the curve. The first term
(k ) provides the smoothing constraints on the level sets by
reducing the total curvature of the level
sets. The second term (c )acts like a balloon force [14] and it
pushes the curve outward towards the object boundary. The goal of
the stopping function is to slow down the evolution when it reaches
the boundaries. However, the evolution of the curve will terminate
only when K=0, i.e., near an ideal edge. In most images, the
gradient values will be different along the edge, thus,
necessitating different K values. In order to circumvent this
issue, the third
geodesic term (. ) is necessary so that the curve is attracted
toward the boundaries ( points toward the middle of the boundary).
This term makes it possible to terminate the evolution process even
if (a) the stopping function has different values along the edges,
and (b) gaps are present in the stopping function. The stopping
term used for the evolution of level sets is given by
, = 1
1 + ( , ,
)
where I(x,y) is the image to be segmented, and k and are
constants. As can be seen, this term K(x,y) is not a function of
and G(x,y) is Gaussian filter transfer function.
4. PROPOSED METHOD:
Consider an iris image to be segmented as shown in Fig. 5(a).
The stopping function K obtained from this image is shown in Fig.
5(b) (In our implementation, k = 2.8 and = 8). As the pupil
segmentation is done prior to segmenting the iris, the stopping
function K is modified by deleting the circular edges because of
the pupillary boundary, resulting in a new stopping function K.
This ensures that the evolving level set is not terminated by the
edges of the papillary boundary [Fig. 5(c)].
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Fig- 5: Stopping function for the GACs. (a) Original iris image.
(b) Stopping function K. (c) Modified stopping function K. [9] The
evaluation equation is ,+1 ,
= ,
, ,
+ , . ,
Where is the time step. In our implementation, is set to 0.05.
The first term on the right-hand side of the above equation is the
velocity term (advection term) and in the case of iris
segmentation, acts as an inflation force. This term can lead to
singularities and, hence, is discretized using upwind finite
differences. [13] The upwind scheme for approximating is given by
where
= A
A = min Dx
i,j, 0
2
+ max Dx+
i,j, 0
2
+ min Dx
i,j, 0
2
+ min Dx+
i,j, 0
2
where Dx
is the first-order backward difference of in the x-direction;
Dx
+ is the first-order forward difference of in the x-direction;
Dy
is the first-order
backward difference of in the y-direction; and Dy+ is
the first-order forward difference of in the y-
direction. The second term Ki,j ki,j
t t is a
curvature based smoothing term and can be discretized using
central differences. In our implementation, c = 0.65 and = 1 for
all iris images. The third geodesic term is also discretized using
the central differences. After evolving the embedding function ,
the curve begins to grow until it satisfies the stopping criterion
defined by the stopping function K. But at times, the contour
continues to evolve in a local
region of the image where the stopping criterion is not strong.
This leads to over-evolution of the contour. This can be avoided by
minimizing the thin plate spline energy of the contour.[15] By
computing the difference in energy between two successive contours,
the evolution scheme can be regulated. If the difference between
the contours is less than a threshold (indicating that the contour
evolution has stopped at most places), then the contour evolution
process is terminated.
5. IMPLEMENTATION OF IRIS SEGMENTATION USING GACS
There are two steps in implementation
5.1 Pupil segmentation
a. To detect the pupillary boundary the eye image is first
smoothened by 2-D Gaussian filter.
b. After that the threshold is decided by M+13. Where M is the
minimum value of gray level in the filtered image. This threshold
is applied on the eye image to find the binary image.
c. Again the 2-D median filter is applied on the image so that
the part remaining in the image by eyelashes can be removed.
d. After median filtering some part in pupillary area remains
black in the binary image due to specular reflection during eye
image acquisition. So for overcoming that the morphological closing
operation is used by Matlab function imclose with disk structure
20.
e. At last the Pupillary boundary is located on the eye
image.
So after all these steps the Pupillary boundary can be located
as shown in figure 6.
(a) (b)
Fig - 6: (a) Eye Image (b) Eye image with Pupillary boundary
localization
5.2 Iris Segmentation
A contour is first initialized near the pupil. The embedding
function is initialized as a signed distance function to (t = 0).
Mesh plot of that function looks like a cone. The evaluation
equation is
i,jt+1
i,j t
t= cKi,j
t Ki,j ki,j
t t + i,jt .Ki,j
t
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After evolving the embedding function according to above
equation, the curve begins to grow until it satisfies the stopping
criterion defined by the stopping function K. 5.2.1 Stopping
function of the eye image
The stopping function K x, y = 1
1+(G x ,y I x ,y
k)
can
be found by applying Gaussian filter G(x,y) on the image and
taking value of constant = 10 and k = 1.6. Now the modified
stopping function (K) is determined by deleting the pupillary
boundary in the stopping function (K). So the evolution of the
curve and the mesh plot of corresponding embedding functions at
final level of iteration as shown in Fig. 7.
(a) (b) Fig-7: Evolution of the GAC during iris segmentation at
final level iteration. (a) Segmented iris image (b) Mesh plot of
Corresponding embedding functions
6. RESULTS
6.1 Application on Image of CASIA Data base
6.1.1 Pupil Segmentation
(a) (b) (c)
(d) (e) Fig-8: (a) Eye Image of CASIA Database, (b) Eye image
after thresholding, (c) Iris-pupil boundary of iris, (d) Stopping
function (K) of eye image, (d) Modified stopping function (K) of
eye image
6.1.2 Segmentation of iris using GACs The Iris Segmentation can
be shown by table given bellow:
Table-1: Evolution of the GAC during iris segmentation at
different iteration
Sr. no.
Number of
iteration
Contours on different
iterations
Corresponding embedding
functions by a mesh plot
1. Initial
2. 200th
3. 1400th
4. 3800th
6.2 Application on Proprietary Database
6.2.1 Pupil Segmentation
(a) (b) (c)
(d) (e) (f)
Fig-9 (a) Eye Image of Proprietary Database, (b) Eye image after
thresholding affected by specular reflection, (c) Thresholding
image after applying morphological closing operation (d) Iris-pupil
boundary of iris, (e) Stopping function (K) of eye image, (f)
Modified stopping function (K) of eye image
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6.2.2 Segmentation of iris using GACs
The Iris Segmentation can be shown by table given bellow:
Table-2: Evolution of the GAC during iris segmentation at different
iteration
Sr. no
.
Number of
iteration
Contours on different
iterations
Corresponding embedding
functions by a mesh plot
1. Initial
Contour
2. 200th
3. 1000th
4. 3400th
7. CONCLUSION
The process of segmenting the iris plays a crucial role in iris
recognition systems. Traditionally, iris systems have employed the
integro-differential operator or its variants to localize the
spatial extent of the iris. In this paper, a novel scheme using GAC
for iris segmentation has been discussed. The GAC scheme is an
evolution procedure that attempts to elicit the limbic boundary of
the iris as well as the contour of the eyelid in order to isolate
the iris texture from its surroundings. Experimental results on the
Proprietary and the CASIA -Interval datasets indicate the benefits
of the proposed algorithm. The
stopping criterion for the evolution of GACs is image
independent and does not take into account the amount of edge
details present in an image. Thus, if the iris edge details are
weak, the contour evolution may not stop at the desired iris
boundary leading to an over segmentation of the iris. Over
segmentation can be avoided by developing an adaptive stopping
criterion for the evolution of the GACs. ACKNOWLEDGEMENT Nothing in
this world can be accomplished without the blessing of God, the
Almighty. Therefore, at the outset, I would like to thank him with
the blessing of whom, this arduous work could take its shape. I
wish to thank Prof. Rekha Vig (EXTC, Department, MPSTME, Mumbai),
Mr. Santosh Kumar Soni for their valuable advice and support.
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BIOGRAPHIES
Kapil Rathor obtained a degree in Master of Technology
(Electronics and communication) from NMIMS, Mumbai in June 2013.
His M.Tech. research is related to Biomtrics and
Image Processing. He has done his research work in C-DAC,
Mumbai. Currently He is working as Assistant professor in St. john
College of Engineering and Technology, Palghar, INDIA (Electronics
and Telecommunication). His one research paper, titled by
Application of Image Processing in Iris Segmentation for a
Biometric System Based on Iris, has been published in International
Journal of Digital Signal and Image Processing (IJDSIP). Also other
research paper titled by Iris collarette boundary localization
using 2-d dft for iris based biometric system has been published in
International journal of Advance research in computer Engineering
and technology (IJARCET).