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Journal of Engineering Science and Technology Vol. 8, No. 4 (2013) 399 - 405 © School of Engineering, Taylor’s University
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EFFICIENT IRIS SEGMENTATION BASED ON EYELID DETECTION
ABDULJALIL RADMAN1,2,*, NASHARUDDIN ZAINAL
1, MAHAMOD ISMAIL
1
1Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built
Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor DE, Malaysia 2Department of Communication and Computer Engineering, Faculty of
Engineering and Information Technology, Taiz University, Taiz, Yemen *Corresponding Author: [email protected]
Abstract
This paper proposes a computationally efficient eyelid detection algorithm for
detecting eyelid boundaries in iris images acquired under less constrained imaging conditions. The proposed eyelid detection algorithm is developed based on the
live-wire technique. The major advantage of the proposed algorithm is its
computational simplicity as compared to the prior eyelid detection algorithms.
The saturation color features of the sclera region of the HSI color space of the iris
image are exploited to determine the two intersection points between each eyelid
and the outer iris boundary. The strongly connected edges between these two points are detected using the live wire technique that is likely to be the eyelid
boundary. The experimental results obtained from UBIRIS.v1 database reveal that
the eyelid detection algorithm which proposed in this paper improves the
segmentation accuracy for the less constrained iris images.
Keywords: Iris segmentation, Eyelid detection, Live-wire.
1. Introduction
The rich and unique textural details of the iris make it the most promising
biometric trait for the personal identification [1]. The majority of iris recognition
systems use iris images taken under less constrained imaging conditions to
guarantee high performance segmentation. However, these constrained conditions
are not appropriate for security applications. Therefore, using unconstrained iris
images represents the promising solution for such kind of applications, but on the
other hand eyelid occlusion may degrade the performance of iris segmentation.
Removing eyelids occlusion in addition to localizing the inner and outer iris
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boundaries represent the main stage in all iris recognition systems, which called
iris segmentation. Eyelid occlusion is one of the challenging noise factors in the
iris segmentation scenario. Most of the eyelid detection algorithms in the
literature are shape-based algorithms in which eyelid boundaries are detected as a
parabola or horizontal lines [2-5]. Active contour models [6, 7] are used to
localize iris boundaries, which make it possible to simultaneously localize the iris
and eyelid boundaries; but in contrast such methods require significant
computation time and many parameters must be carefully chosen to converge an
appropriate curve. In this paper, a shapeless edge-detection algorithm has been
proposed to localize eyelid boundaries. The optimal eyelid boundaries are
localized by means of the live-wire method [8]. The major advantage of the live-
wire method is its ability to be performed in real-time [9]. The proposed
algorithm outperforms on the existing eyelid detection algorithms in the following
advantages: it involves eyelid boundaries with irregular shapes, simple in
understanding and implementation, and significantly rapid.
The rest of this paper is organized as follows. In Section 2, the previous works
are presented. The eyelid detection algorithm using the live wire is described in
Section 3. The experiments and results, the discussion, and the comparison results
are presented in Section 4. Conclusions and future work are provided in Section 5.
2. Previous Works
Daugman [2] has improved his integro-differential operator [10] in order to localize
eyelid boundaries as a parabola; the circular path of the contour integration in the
original integro-differential operator has been changed to parabolic path. Wildes
[11] extracts the upper and lower eyelid boundaries with parabolic arcs also; the
parabolic Hough transform has been used to find eyelid boundaries on the edge map
of the iris image. Using the parabolic Hough transform [3, 4] localises eyelid
boundaries as a parabola. Masek [5] uses the linear Hough transform as a means of
localizing the upper and lower eyelids. The upper and lower eyelids are detected as
lines; then, horizontal lines are utilized to separate eyelid regions from iris region.
These horizontal lines intersect with first lines at the closest points to the pupil on
the outer iris boundary. Min and Park [12] utilizes the parabolic Hough transform as
a means of localizing eyelid boundaries in the normalized iris image instead of the
original iris image, in order to avoid the eye rotation problem and consequently
reduces the dimension of parameter space.
3. Eyelid Detection Algorithm using the Live Wire
This research work has proposed a computationally efficient eyelid detection
algorithm. The proposed eyelid detection algorithm is based on the live wire
technique [8]. Firstly, a radial edge detector based on the circle parameters of the
outer iris boundary is innovated to find the two intersection points between each
eyelid and the outer iris boundary. Through empirical analysis of the scalar
region, which contiguous the iris region, in the HSI color space; we found that the
scalar region is less saturated, which means that the saturation values of the scalar
region much closer to the zero. Thereby, the radial edge detector is used to scan
the outer iris boundary and compute the saturation average value of the ten
adjacent pixels for each pixel on the outer iris boundary; once the average value
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exceeds the threshold value at a certain pixel, this pixel is considered as an
intersection point between the eyelid and the outer iris boundary.
Figure 1 shows an example of detecting the four intersection points between
both the upper and lower eyelids and the outer iris boundary using the proposed
radial edge detector.
Fig. 1. An Example of Detecting the Four Intersection Points between
Eyelids and the Outer Iris Boundary using the Proposed Radial Edge Detector.
The eyelid detection algorithm in this paper is developed based on the live
wire method [8]; the live wire method requires two initial points to indicate the
start and end points of the eyelid. The two intersection points between each eyelid
and the outer iris boundary that have been detected by the radial edge detector are
used as initial points for the live-wire. In order to facilitate the detection process,
the two delimited regions which are used for eyelid boundary detection are
delineated based on the intersection points and the outer iris boundary as depicted
in Fig. 2. Towards delineate the optimal eyelid boundaries; the eyelid detection
region is first pre-processed to associate the edges with low costs. A combination
of edges of the Canny edge detector, gradient magnitude, gradient orientation, and
Laplacian zero-crossing are utilized to calculate these costs.
Fig. 2. The Delimited Regions to be used for Eyelid Boundary Detection.
The local cost function l (p, q) on the edge from pixel up to a neighboring
pixel q is a weighted sum of component cost functions as follows:
)(4.0)(1.0),(1.0)(4.0),( qfqfqpfqfqpl LMOC +++= (1)
where fC, fO, fM, and fL represent the Canny edge detector, gradient orientation,
gradient magnitude, and Laplacian zero-crossing cost functions, respectively. As
shown from Eq. (1), each cost term contributes by a constant weight to the total
cost function; the cost terms are designed to associate the edges that are likely to
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be boundaries with low costs. More details on the live-wire operation and its’ cost
function construction can be found in [8, 13-15]. Automated detection of eyelids
boundaries using the proposed eyelid detection algorithm is illustrated in Fig. 3.
Fig. 3. Examples of Eyelid Boundary Detection using the Proposed Algorithm.
4. Experiments and Results
The proposed eyelid detection algorithm was tested on session 2 iris images of the
UBIRIS.v1 database [16], it includes 662 images. Several noise factors were
introduced, enabling the appearance of heterogeneous images regarding
reflections, contrast, luminosity, eyelid and eyelash iris obstruction and focus
characteristics [17]. We implemented the well-known iris segmentation method
proposed by Wildes [11] with the eyelid detection algorithm described in [11] and
the proposed eyelid detection algorithm. Next, the segmentation accuracy results
of both algorithms were compared to prove the effectiveness of the proposed
eyelid detection algorithm. The ground-truth images of the iris region for all iris
images in the session 2 of UBIRIS.v1 database were manually generated to
perform the evaluation process.
A quantitative evaluation of the classification error rate was adopted to
measure the level of iris segmentation accuracy. The classification error rate was
measured based on the comparison between the ground-truth image and the
binary output image which obtained by the iris segmentation algorithm. The
classification error rate (Ei) [18] measures the difference between the ground-truth
image (G(r,c)) and the binary output image (O(r,c)), which obtained by the iris
segmentation algorithm from the input image (Ii), as follows:
∑∑ ⊗×
== =
r
k
c
li lkGlkO
crE
1 1
),(),(1 (2)
where r and c refer to the image height and width, respectively. The comparison
between the output image and the ground-truth image was achieved by means of the
logical XOR operator, ⊗ . The overall classification error rate of the iris segmentation
algorithm (E) was calculated by the average errors of the input images:
∑==
N
iiE
NE
1
1 (3)
where N is the total of input images. Table 1 shows the segmentation accuracy
results obtained by the Wildes’ method [11] and the Wildes’ method with our
eyelid detection algorithm.
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Table 1. Iris Segmentation Accuracy.
Method Segmentation
accuracy (%)
Wildes’ method 97.13
Wildes’ method with our
eyelid detection method 97.47
It can be observed from the segmentation accuracy summarised in Table. 1
that the proposed eyelid detection algorithm improves the segmentation accuracy
of the Wildes’ method.
Samples of iris segmentation results using the Wildes’ method with Wilde’s
eyelid detection algorithm and our eyelid detection algorithm are presented in
Fig. 4. As clearly observed from Fig. 4 the proposed eyelid detection algorithm
involves eyelid boundaries accurately regardless their shape.
(a)
(b)
Fig. 4. Samples of Iris Segmentation Results using the Wildes’ Method with
(a) Wildes’ Eyelid Detection Algorithm (b) our Eyelid Detection Algorithm.
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5. Conclusions and Future Work
Eyelid occlusion represents one of the noise factors that degrade the performance of
iris segmentation. In this paper, we have proposed a simple and efficient eyelid
detection algorithm. The live-wire technique has been utilized to localize eyelid
boundaries based on the intersection points between the eyelid and outer iris
boundary. Results demonstrate that the proposed eyelid detection algorithm
improves the iris segmentation accuracy. In the future, the reflection noise can be
removed before detecting eyelid boundaries using the live-wire technique which
likely to be affected by such noise. This would improve the segmentation accuracy.
The proposed algorithm has designed to work with color images, but in the future,
our method will likely be used with single channel images.
Acknowledgement
This research has been conducted in the Computer & the Network Security
Laboratory, Universiti Kebangsaan Malaysia (UKM). The authors would like to
thank the University for sponsoring this research through research university grant
UKM-GUP-2011-060.
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