Journal of Engineering Science and Technology Vol. 8, No. 4 (2013) 399 - 405 © School of Engineering, Taylor’s University

EFFICIENT IRIS SEGMENTATION BASED ON EYELID DETECTION ABDULJALIL RADMAN1,2,*, NASHARUDDIN ZAINAL1, MAHAMOD ISMAIL1 1

Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor DE, Malaysia 2

Department 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 399

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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 livewire 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:

l( p, q) = 0.4 fC (q) + 0.1fO ( p, q) + 0.1fM (q) + 0.4 fL (q)

(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:

Ei =

1 r c ∑ ∑ O(k , l ) ⊗ G (k , l ) r × c k =1l =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: E=

1 N ∑ Ei N i =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 Wildes’ method Wildes’ method with our eyelid detection method

Segmentation accuracy (%) 97.13 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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