# A fast and accurate iris segmentation method using an LoG filter and its
zero-crossings

Tariq M. Khan, Donald G. Bailey, and Yinan Kong Tariq M. Khan and Yinan Kong
are with Department of Engineering, Macquarie University, Sydney, Australia
Donald G. Bailey is with the School of Engineering and Advanced Technology,
Massey University, Palmerston North, New Zealand

###### Abstract

This paper presents a hybrid approach to achieve iris localization based on a
Laplacian of Gaussian (LoG) filter, region growing, and zero-crossings of the
LoG filter. In the proposed method, an LoG filter with region growing is used
to detect the pupil region. Subsequently, zero-crossings of the LoG filter are
used to accurately mark the inner and outer circular boundaries. The use of
LoG based blob detection along with zero-crossings makes the inner and outer
circle detection fast and robust. The proposed method has been tested on three
public databases: MMU version 1.0, CASIA-IrisV1 and CASIA-IrisV3- Lamp. The
experimental results demonstrate the segmentation accuracy of the proposed
method. The robustness of the proposed method is also validated in the
presence of noise, such as eyelashes, a reflection of the pupil, Poisson,
Gaussian, speckle and salt-and-pepper noise. The comparison with well-known
methods demonstrates the superior performance of the proposed method’s
accuracy and speed.

###### Index Terms:

Pupil segmentation, Pupil Localization, Region properties

##  I Introduction

The use of fraudulent identities is considered to be a key enabler of serious
organised crime and even terrorism [1, 2, 3]. Biometrics is a fast developing
science that can provide a higher level of security, convenience, and
efficiency to protect against identity theft than traditional password-based
methods for user authentication [4, 5, 6]. Humans have many biometric traits
such as a face, hand geometry, fingerprint, voice and iris [7], that can be
used for identity verification. However, iris recognition is found to be
accurate and one of the more reliable methods due to its high degree of
uniqueness and randomness, even between identical twins, and remains
constantly stable throughout an adult’s life [8, 9].
The iris is a nearly circular shaped region between the sclera and the pupil.
It is an internal organ that is well protected from the environment and can be
seen from outside the body [10]. It consists of many features like furrows,
freckles, stripes, coronas, ligaments, arching, zigzag collarette, ridges,
rings, and crypts [11, 12, 13]. These features are statistically stable,
unique, and are randomly distributed in the human iris region [14, 15]. These
properties make the iris a secure and reliable source of personal
identification [16].
Generally, the essential steps within an iris recognition system are: eye
image acquisition, segmentation of the inner and outer boundary of iris,
extraction of unique features, feature matching and finally the recognition of
a person [8], as shown in Fig. 1. Of these steps, the segmentation of the
inner and outer boundary of iris plays a vital role towards system accuracy.
Iris segmentation localises two different boundaries. First, it segments out
the pupil’s outer boundary, known as the pupillary or inner boundary of the
iris, and then the outer or limbic boundary of the iris. Iris segmentation is
a computationally intensive task in iris recognition [8].
Although state-of-the-art methods [17, 18, 19, 20] are very effective for iris
recognition, their performance is greatly affected by iris segmentation. The
reasons are as follows:

  * •

Iris segmentation defines the contents of the features that are subsequently
processed, by normalisation, feature extraction, and matching. Thus the
accuracy of iris recognition is directly related to iris segmentation.
Inaccurate iris segmentation is reported to be the main cause of failure in
iris recognition systems [13].

  * •

Processing speed is a bottleneck in practical applications, while in iris
recognition the most time-consuming module is iris segmentation [17, 21].

There are several challenges in practical iris segmentation. Some of them are
the image acquisition angle, pupil dilation, occlusion, focus, and image
clarity. Pupillary and limbic boundaries are nonmuscular, which can lead to
inaccuracy if fitted with simple shape assumptions. All of these challenges
makes the iris segmentation process difficult. Therefore a fast, accurate and
robust iris segmentation algorithm is highly desirable.
In the literature, different techniques are proposed for fast and accurate
iris segmentation. These can be divided into two broad categories: Shape-based
detectors [12, 17] and intensity-based thresholding [7, 8, 9]. Generally,
shape-based detection gives better accuracy but is slower in processing [22,
23, 24]. On the other hand, intensity-based threshold methods are fast, but
they are less accurate than the shape-based detectors.
In this chapter, we investigate the combination of a fast shape-based detector
with an intensity-based threshold to accurately segment an iris. A Laplacian
of Gaussian (LoG) filter is used as a shape detector along with region growing
and an intensity-based threshold is efficiently used to locate the true pupil
region. Then the zero-crossings of the LoG filter are used to mark the true
inner and outer boundaries of the iris. The combination of these filters not
only gives better accuracy than intensity-based threshold methods but also
gives much better processing speed than shape-based detectors.

Figure 1: Block diagram of an iris recognition system

The rest of this paper is organized as follows.Background and related work is
presented in Section II. Section III give details of proposed method.
Experimental results are presented in Section IV. Computational cost and
limitation are discussed in section V followed by the conclusion of this
chapter in Section VI.

##  II Background and related work

In iris segmentation, the first step is to find the pupil centre and then to
fit a circle to localise the pupil boundaries [8]. The centre of the iris lies
within the pupil, but the two circles are not concentric. An accurate
localization of the pupil reduces the search space for the centre of the iris.
Duagman proposed an edge-based detector using an integral differential
operator (IDO) [25]. The operator searches over the image domain $(x,y)$ for
the maximum in the blurred partial derivatives with respect to increasing
radius $r$, of the normalised contour integral of $I(x,y)$. The complete
operator behaves as a circular edge detector, blurred at a scale set by
$\sigma$, searching iteratively for the maximal contour integral. A circular
Hough transform is used to detect the inner and outer boundaries of the iris.
Three parameters $(x_{0},y_{0},r)$ are used to define each circle, where
$(x_{0},y_{0})$ is the center and $r$ is the radius of the circle. Similarly,
a mixture of the gradient edge-map and the circular Hough transform was used
by Wildes [12] to pinpoint the iris boundaries.
The literature provides evidence that histogram and threshold based techniques
are faster than using the Hough transform [26]. In histogram-based techniques,
thresholding is used for locating the pupil considering it as the darkest
region in an eye image [7]. For pupil detection, Zhang [27] first divided the
eye image into small rectangular blocks of fixed size and then found the
average intensity value of each block. Dey [28] used down-sampling on the
input image before pupil and iris localization. To find all the connected
components, first, contrast scratching is applied to the down-sampled image
followed by the image binarization.
Ibrahim _et al._ [8] used histogram-based and standard deviation based
adaptive thresholding to localise the pupillary boundary. A range of grey
levels that has the highest probability of the pupil is found by moving a
circular window. The window that contains a grey level peak with a minimum
standard deviation of x- and y- coordinates is selected as the pupil region.
This technique may fail for images containing other objects such as eyelashes,
eyebrows, hair, and possibly the black-frame of glasses. Similarly, Khan _et
al._ [7] used histogram-based thresholding along with eccentricity to extract
the pupillary boundary. Their technique lacks the ability to handle multiple
objects in a given binary image. Use of eccentricity on its own could be
misleading if a small object (other than the pupil) has smaller eccentricity
in an image. To overcome these issues, Jan _et al._ [29] proposed a technique
in which a common eye position is generalised by using integral image
projection functions. Then it was binarized by using an adaptive threshold via
a histogram bisection method. It is controlled by a parameter vector that is
recorded by eccentricity, image statistics, and the two-dimensional (2D)
object geometry. Then the limbic boundary is identified in the horizontal
direction by using gradient information. Lastly, the iris boundaries are
localised by using a mixture of radial gradients. This method gives effective
results for iris databases but with certain limitations. First, the dark
locations (e.g. pupil, etc.) were highlighted because of the property of
integral image projection. If other low-intensity regions (e.g., eyebrows,
eyelashes, hair, and the frame of glasses) block the dark regions (e.g. pupil,
etc.), then the integral image projection may fail to highlight it because of
the coordination failure of the horizontal and vertical projection functions.
Secondly, the useful combination of area and eccentricity of 2D objects are
used to locate the pupil in a binary image but, as was discussed earlier, the
results are better for a perfectly round pupil, but method may not provide the
desired results for a distorted pupil, for example, CASIA- IrisV3-Interval and
CASIA-IrisV4-Thousand iris databases.
Although in literature, many different iris segmentation methods have been
proposed, many of them are either computationally or present relatively high
or unacceptable segmentation error rates. The iris image segmentation
algorithm proposed in this chapter consists of two major modules, namely pupil
detection, and limbic boundary localization including eyelid/eyelash
detection. The implemented algorithms avoid unnecessary processing over image
regions that do not contain relevant information for iris image segmentation,
and consequently iris recognition.

##  III Proposed method

We have proposed a two-stage method for iris localization. In stage 1, the
pupil (iris inner boundary) is localised, an important step in iris
segmentation for two reasons:

  1. 1.

In the iris images, if the pupil is wrongly localised, then there are often
errors in detecting the limbic boundary, as the iris’s outer boundary
localization methods use the pupil circle parameters as inputs [29, 30, 31,
7].

  2. 2.

The time consumed in pupil localization is much more than for the limbic
boundary localization because the whole eye (iris) image is processed in pupil
localization, whereas a sub-image can be processed for the limbic boundary
localization [30, 31].

In stage 2, the limbic boundary is localised. The details of the proposed
method are given in the next subsections.

###  III-A Pupillary boundary localization

There are many methods reported in the literature for detecting circular
objects in an image. Intensity-based thresholding can be treated as blob
detection, in which the pupil is treated as a circular black region on a
bright background. Such techniques assume that the gray-level intensity of the
pupil in an eye image is less than for any other region (e.g., iris, sclera,
and skin parts). Some researchers use this assumption to localise coarse
pixels in the pupil region using a histogram or threshold and then use
gradient-based techniques to segment the boundaries of the iris [7, 8, 32].
These techniques may not work for an eye image containing other low-intensity
regions. When the pupil is shaded by eyelashes, thresholding fails to locate
the true centre and radius of the pupil region. For example, Fig. 2 and 2
clearly show that [8] fails to get a proper pupil region because of noise. To
overcome this, [33] proposed some solutions that make the implementation
computationally expensive. Another disadvantage of [33] is the use of
iterative processes that limit its real-time implementation.

Figure 2: Comparison of [7, 8] with proposed method. (a) and (b) are two noisy
images from the MMU v1 database. (c) and (d) show the pupil extracted by [8].
(e) and (f) show the pupil extracted by the proposed method. (g) and (h) show
the normalised pupil using [8]. (i) and (j) show the normalised pupil by the
proposed method.

To provide a robust, computationally light and non-iterative solution for
finding the disc, we adopted a scheme that combines both a shape-based
detector and an intensity-based threshold. We treated the pupil as a blob and
tune a Laplacian of Gaussian (LoG) [34] operator to detect the edges or blobs
at a particular scale. It is based on filtering an image with a Gaussian of
particular standard deviation $\sigma$, also known as the scale factor. The
2-D LoG function with Gaussian standard deviation $\sigma$ has the form:

$h\left({x,y;\sigma}\right)=-\frac{1}{{\pi{\sigma^{4}}}}\left[{1-\frac{{{x^{2}}+{y^{2}}}}{{2{\sigma^{2}}}}}\right]{e^{-\frac{{\left({{x^{2}}+{y^{2}}}\right)}}{{2{\sigma^{2}}}}}}$
(1)

The scale normalised version of the LoG filtered defined in Eq. 1 is modified
as [34]:

$\begin{array}[]{l}{h_{SN}}\left({x,y;\sigma}\right)={\sigma^{2}}\times
h\left({x,y;\sigma}\right)\\\
\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,=-\frac{1}{{\pi{\sigma^{2}}}}\left[{1-\frac{{{x^{2}}+{y^{2}}}}{{2{\sigma^{2}}}}}\right]{e^{-\frac{{\left({{x^{2}}+{y^{2}}}\right)}}{{2{\sigma^{2}}}}}}\end{array}$
(2)

The selection of $\sigma$ depends on the blob size. A Gaussian has several
advantages that facilitate blob detection. First, the Gaussian is separable;
that makes it computationally efficient. Second, the Gaussian is smooth and
localised in both spatial and frequency domains. This smoothing provides a
good compromise in terms of suppressing false edges. The LoG acts as a
bandpass filter because of its differential and smoothing behaviour. As the
Laplacian is a linear operator, Gaussian filtering followed by differential is
the same as filtering with a Laplacian of Gaussian.
To provide a robust and computationally light solution for finding the disc,
we adopt the following strategy. An iris image $I\left({x,y}\right)$ from the
MMU-V1 database is taken as sample, as shown in Fig. 3. The image contains a
black disc in the centre of the eye with small clumps of undesirable
foreground pixels, e.g. salt noise. Though a median-based operator can be used
to tackle such high-frequency noise, it can be computationally expensive.
Therefore, it can be replaced with a morphological opening that does a similar
job with fewer resources. The smoothed image is shown in Fig. 3.

Figure 3: Preprocessing: (a) Sample input image. (b) Image filtered with
morphological _opening_ with a disc structuring element of radius 5 pixels.

The next processing step is to apply the LoG filter. The LoG filter is an
anisotropic filter that has been used effectively in the past to detect blobs
[35]. For iris segmentation, the pupil circular region can be taken as a black
blob on a white background. To facilitate the LoG application, the iris image
is first converted to a tri-level image. In the tri-level image, the pupil is
represented by black intensity, the outer circular region around the pupil
with white intensity, and the rest of the image as grey intensity. This
particular swapping of the white and grey level regions facilitates the
application of the LoG filter, by giving a larger contrast between the inner
and outer circles as shown in Fig. 4.

Figure 4: Median Filtering: (a) Sample input image. (b) 2D representation of a
LoG filter. (c) image (a) converted to a tri-level image

Conversion to a tri-level image requires two thresholds: T1 and T2. The first
threshold T1 is chosen as a level below which we have a large confidence of
picking the pupil. The second threshold T2 is an intensity level beyond which
we have a high confidence to get the rest of the image. The result is
therefore given by

$Tri_{img}=\left\\{\begin{array}[]{l}0\,\,\,\,\,\,\,\,\,\,\,\,if\,\,\,{I_{smooth}}<T1\\\
1\,\,\,\,\,\,\,\,\,\,\,\,if\,\,\,T1<{I_{smooth}}<T2\\\
0.5\,\,\,\,\,\,\,\,if\,\,\,\,\,{I_{smooth}}>T2\end{array}\right.$ (3)

The problem of choosing the thresholds has been facilitated by preprocessing
the image with morphological operator opening. This greatly reduces the stress
of finding accurate thresholds $T_{1}$ and $T_{2}$. For the sake of
experiments, we choose T1=0.2 and T2=0.5 for a scaled IRIS image in the [0-1]
range. The tri-level intensity converted image is displayed in Fig. 4. The
figure shows the effectiveness of tri-level scaling.
A LoG filter given in Eq. 1 is applied on ${Tri_{img}}$ as

${I_{LoG}}={Tri_{img}}*h\left({x,y,\sigma}\right),$ (4)

where $\sigma=R$ is the average pupil radius of a particular database. By
applying this LoG with such a coarse scale, the output images ${I_{LoG}}$
possesses strong contours due to the heavy smoothing, as shown in Fig. 5. The
LoG filtering provides the maximum response in the pupil region. Now a mask is
created that corresponds to the pixel of ${I_{LoG}}$ with maximum response as

${Ig_{mask}}=\left\\{\begin{array}[]{l}1,\,\,\,\,\,\,if\,\,\,\,{I_{LoG}}>{\lambda_{a}}\\\
0,\,\,\,\,\,\,otherwise\end{array}\right..$ (5)

where ${\lambda_{a}}$ is set to some value above the mid-grey level, such as
0.6 for $I_{LoG}$ scaled in [0-1] range. Fig. LABEL:LoG:-b shows the mask
image that is used for generating the first seed image. This mask image
${Ig_{mask}}$ is then multiplied pixel-wise by the ${I_{smooth}}$ image to get
the first seed image as

${Ig_{seed}}={I_{smooth}}.*{Ig_{mask}}$ (6)

Figure 5: LoG filtering: (a) LoG filtered image. (b) seed image created from
threshold image. (c) Segmented pupil by proposed method

Fig. 5 shows that this seed image contains the pupil. The next task is to
choose an appropriate seed point among this seed image using centroid of the
seed image. This point is used as a seed point for the region growing method
[36]. Region growing is a segmentation strategy that starts with a pool of
only one initial seed point and then adds more pixels to the pool that are
8-connected neighbours with similar intensity to that of the earlier seed
points. The tri-level image loses important texture information, therefore,
the LoG-filtered image is used only for finding seed point where the region
growing is performed on the $I_{smooth}$. The initial seed point is grown to a
target pool of pixels using a similarity measure where the intensity of the
seed point is compared with 8 neighbours using a 5 percent rule. Fig. 5 shows
a segmented pupil using the proposed method. By using LoG filtering along with
region growing, most of the problems reported in [7, 8, 32] for pupil
segmentation are addressed.
From Fig. 2 and 2 it can be observed that the pupil region reconstructed by
the proposed method is an improvement on [7, 8, 32]. But in extremely noisy
conditions, this still can give some error in finding the accurate centre and
radius of the pupil, Fig. 6 provides evidence of this situation. Therefore, to
further strengthen the proposed method, zero-crossings of the LoG-filtered
image are also obtained. The zero-crossings give the true edges of the
pupillary boundary. This can help the proposed method to find more accurately
the centre and radius of the pupil.
The behaviour of the LoG zero-crossings edge detector is largely governed by
the standard deviation of the Gaussian used in the LoG filter. It is common to
see several spurious edges detected away from any obvious edges. To deal with
spurious edges the first order differential information of the image is
required. This information will provide The gradient magnitude at the zero
crossing of the LoG-filtered image. Discarding the zero crossings with a
magnitude lower than a threshold will retain only the stronger edges. To
implement the zero-crossings of the LoG filter, the morphological filtered
image ${I_{smooth}}$ is convolved with a LoG filter with $\sigma=2$ , having
filter size $n=\left\lfloor{(3\sigma)\times 2+1}\right\rfloor$. Then the zero-
crossings of this LoG filtered image are obtained with a threshold of
${\lambda_{c}}=0.15$ for the MMU v1 database. Fig. 7 shows the result of zero-
crossings. The images of the database are preprocessed with Gaussian smoothing
before calculating their first order differential strength measure. A single
value of ${\lambda_{c}}$ is appropriate to suppress spurious edges related to
insignificant zero crossing points. The value of ${\lambda_{c}}$ is chosen
after performing several experiments on the MMU-v1 database. It is observed
that if the value of ${\lambda_{c}}$ is increased then it gives fewer edges,
and in some cases it affects the pupil and iris boundary edges. On the other
hand, if a smaller value is chosen then noise in the zero-crossing image
increases and makes it harder to locate the pupil. From Fig. 7 it can be seen
that the zero-crossings image has unwanted noise that needs to be cleaned up.
Connected components of the zero-crossing image are found and those with fewer
fifty pixels are removed. This removes small unwanted regions without
affecting the pupil and iris boundaries, as shown in Fig. 7. The seed point
calculated for the region growing is used as a reference point to extract the
true circle from the zero-crossings. From this reference point, the boundary
of the pupil is scanned radially in the zero crossing image. From Fig. 6 it
can be seen that the circle is broken because of eyelashes and eyelids. The
zero-crossings help to find the true broken region. By using interpolation,
the remaining part of the circle can easily be predicted.

Figure 6: LoG filtering on noisy image: (a) Sample noisy image of MMU v1
database. (b) Detected pupil by proposed method. (c) Zero-crossing of LoG
filtered image.

Figure 7: LoG zero crossing: (a) Zero crossing without morphological
filtering. (b) Zero crossing with morphological filtering. (c) Zero crossing
after cleaning with area property.

###  III-B Limbic boundary localization

Limbic boundary extraction is also difficult for the following reasons: first,
the eyelids and eyelashes may partially occlude the iris outer boundary.
Second, the contrast between the iris and sclera regions is usually low.
Lastly, as the pupil always exists within the iris region, the pupil and
limbic boundaries could be assumed as two nonconcentric circles; however this
assumption is not always true [26]. Basit [32] picked a horizontal line from
the centre of the pupil and used a gradient to find the edges. Based on these
edges, the pupil centre and the radius are calculated. [7, 8, 37] used a
similar technique in which two secure regions are defined. Then their
gradients are computed, followed by excluding wrong boundary points using a
distance error transform. One disadvantage of these techniques is that they
bias their localization of the limbic boundary in the horizontal direction.
Also, these techniques may not work for an eye image having low-intensity
regions.
To resolve these issues, we adopted a scheme that first finds the true
orientation of the eye in the image. First, $I_{smooth}$ is filtered using a
LoG filter of $\sigma=R$. This is then thresholded to select the top 70% of
positive values. The largest connected component that overlaps the detected
pupil is then selected, as shown in Fig. 8. The detected eye image is
approximately ellipse shaped with major axis almost double the minor axis. The
orientation of the eye is found from the orientation of the major axis of the
ellipse. There are several advantages to finding the true orientation of the
eye. First, if the major axis is treated as the x-axis [7, 8, 37] then the
chances of getting noise in the stable zone will be very low. This will
certainly increase the accuracy of these algorithms. It also makes it easier
to find the area affected by the eyelashes and eyelid (occlusion zone).
Second, the orientation also facilitates the iris normalisation and matching
process.
To extract the outer boundary of the iris, the eye image is divided into four
regions: left stable zone, right stable zone, upper occlusion zone, and lower
occlusion zone, as shown in Fig. 9. Usually, the upper occlusion zone affects
the iris region more than lower occlusion because of the eyelashes and eyelid.
The detection of these zones not only plays a vital role in limbic boundary
localization but also in iris normalisation. In this chapter, this issue is
addressed by first detecting the true outer boundary of the iris in the secure
regions and then in the occlusion zones. To accurately detect the outer
boundary of the iris, the cleaned zero-crossing image is used. Both secured
zones are radially scanned in a similar manner as in [7, 37]. Fig. 10 shows
the iris outer boundary extracted from the secured region. The average
distance of the outer boundary in the stable zones from the centre of the
pupil is calculated and is designated as the iris radius $I_{radius}$. Using
this radius, the search is extended into both occlusion zones. Any
discontinuity shows that the region is affected by eyelids/eyelashes, as shown
in Fig. 10. Therefore, using these boundary points, the affected region in the
iris can easily be marked, as shown in Fig. 9. Finally, the disconnected outer
circle is interpolated to give the true centre and radius of the limbic
boundary, as shown in Fig. 10.

Figure 8: Binary Mask used to find out the true orientation of eye image. (a),
(b), and (c) are three sample eye images of MMU database. (d), (e), and (f)
are the binary masks of (a), (b), and (c) obtain by using LoG filtering.

Figure 9: (a) Iris image divided into different zones, (b) Zero-crossings of
LoG filtered image. (c) Detected eyelashes in iris and pupil region.

Figure 10: (a) Extracted secure region. (b) Extended search of boundaries in
turbulence zones. (c) Interpolated iris.

##  IV Experimental results

The validity of the proposed method is evaluated on three public databases,
namely: MMU version 1.0 database [38], CASIA-IrisV1 database [39] and CASIA-
IrisV3-Lamp database [39]. The accuracy rate is used to measure the
performance of the proposed method. The accuracy rate $(A_{r})$ is based on
the accuracy error $A_{e}$, which is defined as

${A_{e}}=\frac{{\left|{{N_{a}}-{N_{\det}}}\right|}}{{{N_{total}}}}\times 100,$
(7)

where $N_{det}$ and $N_{a}$ are the numbers of detected and actual iris
pixels, respectively. The actual iris pixels are calculated manually as
suggested in [8]. If $A_{e}$ is less than 10%, then the detected iris is
considered to be the true iris. $A_{r}$ is defined as

${A_{r}}=\frac{{{N_{success}}}}{{{N_{total}}}}\times 100,$ (8)

where $N_{total}$ is the total number of images in the database and
$N_{success}$ is the total number of eye images in which the iris has been
successfully localized. The following sections describe the details of the
experimental results.

###  IV-A Experimental setup 1

In this experiment, results are collected using data from the MMU version 1.0
database. This database contains 450 images of 45 subjects, i.e., 10 images
per subject. The resolution of each image is 320$\times$240 pixels. The
proposed method has been tested on the whole database. An accuracy rate of
100% is achieved on both the inner and the outer boundary of the iris. Fig. 11
shows the results of the proposed method on some of the randomly selected
images from this. Table I compares the accuracy of the proposed method with
several existing methods on the MMU version 1.0 database.

TABLE I: Comparison of some recent segmentation algorithms applied to the MMU database (Results are taken from [28]). Method |  Accuracy
---|---
Masek [40] |  83.9%
Daugman [25] |  85.6%
Ma et. al. [13] |  91%
Daugman [14] |  98.2%
Somanth et. al. [28] |  98.4%
Proposed |  100%

Figure 11: Results of proposed method on some noisy images of the MMU version
1 database.

###  IV-B Experimental setup 2

The second experiment is performed on the CASIA Ver 1.0 iris database. This
database contains 756 eye images of 108 subjects, 7 images per individual. The
resolution of each image is 320$\times$280 pixels. Using the mixture of LoG
filtering and zero-crossings of LoG filter we achieve 100% accuracy on this
database for both the inner and the outer boundary of the iris. Fig. 12 shows
the results of the proposed method on some of the randomly selected images
from CASIA-IrisV1. Table II compares the proposed method with existing methods
using CASIA-IrisV1.

TABLE II: Comparison of some recent segmentation algorithms over the CASIA 1.0 database (Results taken from the published work). Method |  Accuracy
---|---
Mateo [41] |  95%
Yuan,W [42] |  99.45%
Wildes [12] |  99.9%
Cui [43] |  99.34%
Daugman [11] |  98.6%
A.Basit [32] |  99.6%
Proposed |  100%

Figure 12: Results of proposed method on some noisy images from the CASIA Ver
1.0 database.

###  IV-C Experimental setup 3

In this experiment, results are collected using data from the CASIA-
IrisV3-Lamp iris database. This database contains 16440 images of 441
subjects. Each subject contributed 40 images, 20 images of the each eye with a
resolution of 640$\times$480 pixels. The proposed method was tested on images
from the first 102 subjects. The accuracy rate using the CASIA-IrisV3-Lamp
database is 99.68%. Table III shows the accuracy rate of the proposed method
on CASIA-IrisV3-Lamp database. Fig. 13 shows the results on some randomly
selected images from the CASIA-IrisV3-Lamp database.

TABLE III: Comparison of some recent segmentation algorithms using the CASIA-IrisV3-Lamp (Results taken from [8]). Method |  Accuracy
---|---
Masek [40] |  79.02%
Ibrahim [8] |  98.28%
Proposed |  99.55%

Figure 13: Results of proposed method on some noisy images from the CASIA-
IrisV3-Lamp database.

##  V Computational cost and limitations

The average computational cost is computed for 100 randomly selected eye
images from the MMU version 1.0, CASIA Ver 1.0, and CASIA-IrisV3-Lamp
databases. MATLAB built-in facility is utilised to obtain the optimal results.
Table IV shows a comparison on the computational cost of the proposed method
and three similar states of the art method.
In the presence of dense eyelashes and eyebrows, as shown in Fig. 14, the
proposed method fails to locate the true pupillary boundary. Such dense
eyebrows and eyelashes effect the LoG filtering along with the region growing.
Although, this can be handled by setting a different $\sigma$ for LoG filter
and tuning the region growing, for the generic parameters that are set for the
whole database, it fails to locate true pupillary boundary. Similarly, if the
iris region is affected with dense eyelashes then a single value of
${\lambda_{c}}$ fail to suppress the effect of eyelashes in the iris region.

Figure 14: Iris image severally occluded by the eyelashes and eyebrows. TABLE IV: Processing speed (in seconds) comparison of the proposed with some of the existing methods Method |  MMU version 1.0 |  CASIA Ver 1 |  CASIA-IrisV3-Lamp
---|---|---|---
Ibrahim [8] |  0.95 |  1.1 |  2.2
Jan [29] |  1.5 |  1.7 |  3.0
Labati [44] |  2.6 |  3.0 |  Not available
Proposed |  0.44 |  0.5 |  1.4

##  VI Conclusion

This chapter presents a fast and an accurate iris segmentation technique for
iris biometrics. There are four major contributions. First, we develop a fast
and novel method for pupil segmentation that is based on a shape detector and
an intensity-based threshold. The use of a LoG filter followed by region
growing gives an estimate of the pupil centre and radius.
The true pupillary boundary is refined using the zero-crossings of a second
LoG filter. Next, the true orientation of the eye in the image is estimated
using a third LoG filter. The orientation facilitates the process of locating
the true limbic boundary of the iris and eyelids. Using the zero-crossings of
the LoG, the search is initially started from the stable zones and then
extended to the occlusion zones. The discontinuities are located, which give
indications of eyelids in the iris region. Finally, using the interpolation
the iris outer boundary as well as eyelid arcs are estimated. The proposed
method also works well in estimating the inner and outer boundary of the iris
in the case of partially opened eye images and scattered eyelashes.
The extensive experimental results on three iris databases show that the
proposed method is computationally less expensive in achieving the state-of-
the-art iris segmentation accuracy.

## Acknowledgments

The authors wish to thank the Chinese Academy of Science-Institute of
Automation for providing CASIA ver 1.0 and CASIA ver 3.0 iris database. The
author also wish to thank the Multimedia University for providing MMU iris
database.

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