Robust and Fast Iris Localization Using Contrast Stretching ... - IJETTCS

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 3, Issue 2, March – April 2014 ISSN 2278-6856

Robust and Fast Iris Localization Using Contrast Stretching and Leading Edge Detection Iman A. Saad1,2 , Loay E. George3 1

Department of Mathematics, College of Science, University of Alepo, Alepo, Syria 2 Electronic Computer Center, Al-Mustansiriyah University, Baghdad, Iraq 3 Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq Abstract: Accurate iris segmentation is a key stage of iris recognition. The iris image may hold irrelevant parts (like, eyelid, eyelashes, boundary of pupil) beside to iris. In this paper, a robust method for iris segmentation is introduced. However, the iris segmentation stage frequently fails when iris image doesn’t hold sufficient intensity density between pupil/iris and iris/sclera areas. The proposed method had included a preprocessing step to improve the contrast of the eye region using histogram stretching. This step improves the contrast between the different eye regions, which in turn will facilitate the process of assigning the optimal threshold value that required for doing successful image binarization. Seed filling algorithm is used to locate the pupil as the darkest central segment. Later, a circle fitting method is used to locate the best pupil circle. Also, leading edge detection mechanism is used to detect the outer iris boundary (iris/sclera boundary). A set of tests was conducted on the iris data sets CASIA v1.0 and CASIA v4.0- interval, and the results indicated that with proposed method was able to localize iris with 100% accuracy rate.

Keywords: Histogram stretching, Iris segmentation, Seed Filling, circle fitting.

1. INTRODUCTION In recent year, biometric features have received great attention for many applications, such as face, voice, fingerprints, palmprint, retina, iris, and so on [1]. Among of this biometrics, iris has achieved highest recognition accuracy, because it is has many properties that make it a wonderful biometric identification technology: (i) the textures of iris are unique to each subject all over the world; (ii) The textures of iris are essentially stable and reliable throughout ones' life; (iii) Genetic independence; irises not only differ between identical twins, but also between the left and right eye [2][3]. After Flom and Safir presented the first relevant methodology in 1987, many other methods have been proposed [4]. In the segmentation stage, Daugman introduced an integro-differential operator in 1993 to find both the iris inner and outer borders, this process proved to be very effective on images with clear intensity separability between iris, pupil and sclera regions [5]. Integro-differential operator was proposed with some differences in 2004 by Nishino and Nayar [6]. Two stages of the iris segmentation methods were proposed by Wildes [7]: a gradient based binary edge map is first constructed from the intensity image, and next the Volume 3, Issue 2 March – April 2014

inner / outer boundaries are detected using Hough transform. Other famous iris localization algorithms are based on using Hough transform with combination of Canny edge detection in [8][9], also with integrodifferential operator in [10], and with Haar wavelet transform in [11]. Liam et al. [12] have proposed a simple method on the basis of threshold and function maximization in order to obtain two ring parameters corresponding to the iris inner and outer borders. Although Although these methods have promising performance, they they need to search the iris boundaries overlarge parameter parameter space exhaustively, which takes more computational time. Moreover, they may results in circle detection failure, because some chosen threshold values used for edge detection cause critical edge points being removed. Du et al have proposed an iris detection method on the basis of prior pupil identification. The image is then then transformed into polar coordinates, and the iris outer border is identified as the largest horizontal edge resultant resultant from Sobel Filtering. This approach may fail in the case of non-concentric iris and pupil, as well as in very very dark iris textures [13]. Ghassan et al. has been developed the angular integral projection function as a general function to perform integral projection along angular directions [14]. There is attributes (contrast, brightness and existing noise) are highly sensitive to the specific characteristics of each image. This high sensitivity was the main motivation behind proposing an accurate and fast iris segmentation method that less constrained image capture environments.

2. PROPOSED METHOD As presented in figure (1), the proposed method for accurate iris localization, passes through three main stages: image enhancement, localization of pupil (inner) boundary and finally localization of iris (outer) boundary.

Figure 1: Block diagram of the proposed iris localization method. Page 61

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 3, Issue 2, March – April 2014 ISSN 2278-6856 2.1 Normalization The suitable process could be used to enhance the contrast and brightness of the iris image is the normalization. Contrast stretching (or histogram stretching) is a sort of normalization process. The purpose of histogram stretching is usually to bring the iris image into an intensity range that is more normal or suitable to human vision. Through histogram stretching the image contrast is increased. Usually, the pupil who represents the darkest portion in the human eye is almost located near iris image center; this will help us to reduce the effect of the dark areas caused by eyelashes by considered a sub-image that contains the largest part of the region of interest to perform histogram analysis without need to take the whole eye image. The histogram of the sub-image as shown in figure (2c) can be modified by a linear mapping function, which will stretch the histogram of the sub-image. The applied mapping function for histogram stretching can be found by the equation (1); it maps the minimum gray level Gmin in the image to zero and the maximum gray level Gmax to 255, the other gray levels are remapped linearly between 0 and 255:

Accumulative histogram is a good way to gain the best values of Gmin and Gmax through using a predefined cut-off fraction parameter of the accumulative histogram. If the gray-level histogram is given by H, then the accumulative histogram is determined using the following:

Then, the array H is scanned upward from 0 until the first intensity value corresponds to accumulative histogram above the (Cut-off Fraction x HAcm) is met; this value is considered Gmin. Similarly, H is scanned downward from 255 until the first intensity value corresponds to accumulative histogram less than the (Cut-off Fraction x HAcm) is found this intensity value defines Gmax. Figures (1b, 1d)) show the image after enhancement and its histogram.

Figure 2: Image histograms,(a) Original image, (b) Enhancement image, (c), Histogram of the original image H, (d) Histogram of the enhanced image H', (e) New enhanced histogram H'new, (f) Smoothed histogram H′smooth. 2.2 Localization of Inner Boundary The iris inner boundary is determined by finding the pupil; this step is accomplished by assuming that the pupil region is the darkest circular area in the iris image. The inner boundary localization process is accomplished through the following steps: Step 1: Thresholding The value of optimal threshold is estimated using the image histogram; the latter is represented by a smooth curve that best fits the measured image histogram. This process is less susceptible to the noise may exist in the raw data. As shown in figure (2d), due to histogram stretching many of the stretched histogram, H'new, elements have appeared with zero values. So, to manipulate this case, we replaced these zero valued elements with the interpolation values that depend on previous and next non-zero neighbour histogram values, see figure (2e). Then, as next step, the smoothing (averaging) process is applied on H'new to overcome the existing irregularities may appear in its shape, this step will lead to gain a new smoothed histogram H′smooth , see figure (2f). The goal of all above operations is to determine the best threshold at specific range of the histogram. A reasonable threshold value (T) could assessed depending on H′smooth; this assessment is done by making a scan through H′smooth to find the gray level value corresponds to lowest histogram value within a specified range of gray level. Then, the assessed threshold value is used to convert the iris image into black (0) and white (255) image using the following binarization criterion:

Where, G′(x,y) is the pixel’s intensity and Gbin(x,y) is the mapped pixel value. This process will produce image segmentation, such that the largest semi-circular black segment represents the nominated pupil region, see figure 3. Volume 3, Issue 2 March – April 2014

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 3, Issue 2, March – April 2014 ISSN 2278-6856

Figure 3: Image after thresholding, (a) sample image from CASIA V3.0, (b) sample image from CASIA V1.0. Step 2: Image Denoising Reflection spots may found near (or on) the pupil edge, for such case the pupil boundary points cannot allocated correctly, which of course degrades the accuracy of the detected approximate center and radius parameters of pupil, see figure (4c). In order to overcome this problem, the averaging filter (3×3) was applied on the, Gbin , image, see figure (4d).

neighbours of this seed, and adding any found connected black neighbour to the collected region set. The seed is then removed from the search domain (i.e., Gbin) and all merged neighbours are added to the collected region set. The region growing process continues scanning the neighbours of all pixels listed in the region set until all connected points are tabulated in the region set. Then, the collected seed set is checked, if it is the largest black segment then the pixels collected in the set are saved in an array (denoted SEG array), and considered as the most nominated pupil region, as shown in figure (4e). Then, the coordinates of pupil's center point, P(XC,YC), is determined as the center of SEG pupil’s segment. Then, by scanning the coordinates of the pixels belong to SEG, the system will identify the values of XLeft and XRight sides; because they are represented minimum and maximum found values of the x-coordinate of the pixels belong to SEG, see figure 5. Then, the mid value of the border point’s coordinates (i.e., XLeft & XRight) is determined and considered as the x-coordinate of the pupil center (XC), that is: Similarly, we scan the collected segment (SEG) to identify the locations of YTop and YBottom sides; which are the Ymin and Ymax values, respectively. Then, the y-coordinate of the midpoint is determined and used the y-coordinate of the pupil center point: From this pupil center point, P(XC,YC), we can obtain the horizontal radius (RX),vertical radius (RY) and then calculate the average initial pupil's radius (RInitial) using the following equations:

Figure 5: Pupil initial center and radius Figure 4: Image segmentation, (a) Original image, (b) Enhanced image, (c) Image after thresholding, (d) smoothing the threshold image, (e) Detect max pupil’s segment, (f) Pupil filling. Step 3: Localization of Circular Pupil Boundary In this step, the seed fill algorithm is used as region growing tool for extracting the binary iris image and detects the pupil as the largest black segment. The region growing method consists of picking an arbitrary seed pixel from the set, investigating all 4-connected Volume 3, Issue 2 March – April 2014

Step 4: Fill Pupil Practically, the images belong to iris database CASIA v4.0 have eight white circular spots in the pupil. In order to remove the effect of specular spot reflections, the whole pupil area should be filled with black color. The scan should be applied from outside to inside; during the scan the color of each found white pixel convert to black. The scan starts from the segment (SEG) boundary points till reaching the center of the pupil. Figure 6 presents an example of the pupil filling process.

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 3, Issue 2, March – April 2014 ISSN 2278-6856

Figure 6: Pupil filling. (a) Pupil segment, (b, c) fill pupil by horizontal scan and (d, e) fill pupil by vertical scan. Step 5: Circle Fitting It is easy allocate the pupil boundary accurately after getting: (i) the approximate location of the pupil; that is (XC, YC), as the initial center, and (ii) RInitial as the initial radius. The Circle Fitting Algorithm (CFA) is illustrated in figure 7. That is, for given values of (XC, YC) and (RInitial), the objective of circle fitting is to asses, more accurately, the circle parameters that best fit the collected SEG pupil’s segment. In order to reduce the search time, the CFA starts searching the pupil boundary at a point of the 75% distance relative to the RInitial from the (XC, YC). The algorithm tests the circle to ensure that most of its points lay in the pupil segment, in case of finding most of the circle points are black then the algorithm increases the radius by 1 and recheck if its points are black or not, This step is repeated till reaching the case that a significant ratio of circle pixels are white, then the algorithm tries to move the circle center to the left, right, up, down, and to the other four diagonal directions, if any of them led to meet black circle (i.e., most points are inside the pupil area) then the algorithm will continue to increase the radius; and if it is not then the algorithm will stop. The CFA returns the pupil's radius (RP) and the adjusted center coordinates of the fitted circle.

Figure 7: Circle fitting. Since the pupil’s boundary could be approximate as a circle shape, therefore we need to check if the (SEG) pupil's segment after applying the circle fitting satisfies the circular shape or not through using the following criteria: Where R is the pupil’s radius RP, Area is representing the total number of the SEG pupil's segment. So, if the results of this criteria is close to 12.5, this indicates that SEG pupil segment have a circular shape, as shown in figure (8a, b). But, in case (CS) value is far from 12.5, this indicates that SEG is not circular, this Volume 3, Issue 2 March – April 2014

may happen when the collected SEG segment points may include points belong to the intersected eyelashes with pupil; the collection of the extended eyelashes black points (but connected with the pupil) will introduce an error in the determination of the pupil location, as shown in figure (8c); to handle this case we have to re-calculate the pupil radius by making horizontal scanning in pupil's segment from bottom to top to avoid the effects of eyelashes, and compare the number of black pixels in each line with those in the surrounding horizontal lines; the scan is continued till reaching a horizontal line whose number of black pixels is equal or greater than those of the neighbour lines; then consider this line as the diameter of the SEG pupil's segment, see figure(8c), then determine the radius (as diameter/2), and calculate the new pupil center according to this new radius. After that, the fitting circle algorithm is applied again to obtain the pupil boundary depending on the last calculated radius and center, as shown in figure (8d).

Figure 8: The pupil boundary localization: (a) SEG pupil's segment has circular shape (b) Correct blue circle fitting boundary. (c) SEG pupil's segment has not circular shape induced by eyelashes and the new green diameter of the max segment, (d) Incorrect yellow circle fitting and correct blue circle fitting boundary. 2.3 Localization of Outer Boundary The results of the localization process of the pupil (inner) boundary are used as guiding parameters for initiating the parameters of the detection process of the iris outer boundary. The detection process is done by making a scan along an inclined line segment that slightly below the horizontal line. Following an inclined line instead of horizontal line is to avoid possible occlusions of eyelashes. Through our checks to a large number of iris samples we have noticed that the adoption of inclined lines to down- left or down-right will insure the case of transition from iris to sclera regions. During the scanning process along the inclined line a leading edge detection algorithm was applied through the following steps:  Apply smoothing filtering on the image; by using mean filter. Page 64

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 3, Issue 2, March – April 2014 ISSN 2278-6856  Calculate the average value (Avg) of the points extended along the inclined line in the smooth image, see figure (9b).  Compare each point (P) along the inclined line with average value (Avg): if the value of (P) value is below Avg then set (P) value to zero, else set (P) value to one, as shown in figure (9c).  Remove the small gaps or pores (i.e., short runs of zeros or ones) may found in the binary sequence of (p); this step will lead to long run of zeros followed long run of ones, see figure (9d).  Search for transition point (from zero to one) along the inclines scan line; this point will be considered as a boundary point Piris(x,y) between iris and sclera region , see figure (9e).  Determine the distance between Piris(x, y) and the pupil center point P(xc, yc), then subtract of it the pupil's radius (RP). The resulted distance will be considered as the iris radius of the iris region (RI).

Figure 9: Outer boundary localization. (a) Iris image, (b) smoothed image, (c) graph of series of zero and one, (d) graph of smoothed series of zero and one, (e) detect of iris outer boundary.

3. EXPERIMENTAL RESSULTS The performance of the proposed method is tested using CASIA v1.0 (15) database; it includes iris image samples belong to 108 individuals, with totally 756 images with resolution (320x280) pixels. Also, iris database CASIA v4.0-interval (16) was used in our experiments; it consists of 2639 iris images captured from 249 individuals with (320x280) pixels’ resolution. The conducted tests have been applied on the whole database sets’ images. The Volume 3, Issue 2 March – April 2014

tests results indicated that the proposed system is capable to make an accurate and fast iris localization task. Because of the position of the light source, original iris images may have low contrast and non-uniform illumination, and this will impair the iris segmentation’s result. Therefore, we must enhance the images for getting a uniform distributed illumination and better contrast by means of histogram stretching; the cutoff fraction parameter was set to be value between [0.025-0.04]. Also this enhancement step will help us to get the flexible threshold value automatically for applying image binarization and segment the pupil from other image’s parts; see figures (10b) and (11b). The seed fill algorithm is used to collect the large central black segment found in the image, and consider it the initial allocation of pupil, then the specular spot reflection areas in the collected black segment are found and removed by filling the pupil with black color. As next step, the center point and radius of the segment are determined and considered as the initial parameters of the pupil segment. Then the circle fitting algorithm is applied to get more accurate values for pupil center point and radius. For some of the tested images, the applied method didn't lead to collected segments (SEG) have circular shape because some of the eyelashes are intersected with pupil boundary, so handle such cases a circular shape testing criterion is applied to check whether SEG has a circular shape or not; if it is not an extra processing step is applied to overcome the effect of potential occlusion of the dark pupil's segment that caused by eyelashes. The Leading Edge Detection algorithm was applied for detecting the iris outer boundary. The proposed method has shown a good performance for the iris outer boundary detection because the enhancement process that applied on the iris images makes them have good contrast between the iris/sclera Figures 10 and 11 show the iris segmentation results for different iris images taken from CASIA v1.0 and CASIA v4.0-interval database sets. Depending on the visual evaluation, we have carefully checked the iris segmentation results for all tested images belong to both database sets. Finally, the average processing times of iris segmentation for CASIA v1.0 and CASIA v4.0-interval was computed and found them (0.23ms, 0.25 ms) respectively. These results were obtained by running the iris segmentation process using Visual Basic (6) programming language to develop the proposed system program, and the tests applied on computer platform has 2.4 GHz Core i5 processor, 2 GB RAM. Table 1, shows the results of the proposed iris segmentation using CASIA v1.0 database compared with other methods, and table 2, shows the results of iris detection of our proposed method using CASIA v4.0interval database compared with other methods. Taking into consideration that the used images belong to CASIA v4.0-interval database are the same images belong to CASIA v3.0-interval database.

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 3, Issue 2, March – April 2014 ISSN 2278-6856

Table 2: Performance of iris segmentation for some methods which introduced by different researchers using CASIA v4.0-interval database. Iris Segmentation Sreecholpech C. and Thainimit S. (22) Hong-Lin W. et al.(23) Talebi S. et al (24) Ann A. et al.(25) Basit, A. et al. (20) Proposed Method

Accuracy rate 92.44% 97.29 98.20% 98.85% 99.21% 100%

4. CONCLUSION

Figure 10: Iris Segmentation.(a) original images from CASIA V1.0, (b) binary enhanced image and segmented pupil, (c) Enhanced image with inner and outer iris boundary detection.

The method introduced in this paper can potentially facilitate the iris segmentation task. The test results indicate that proposed method gained a high correct iris segmentation rate with a lower averaged segmentation time of each database sets’ iris image. We have used CASIA v1.0 images, as first test step, to incorporate few types of noise, which almost exclusively related with eyelid and eyelash obstruction. Secondly, we have used CASIA v4.0-interval, because it contains heterogeneous images, also its iris images contains several types of noise (regarding focus, contrast, or brightness, poor image quality and illumination). The proposed method was proceeded in three stages; image enhancement, inner boundary detection and outer boundary detection. Experiments on all CASIA v1.0 and CASIA v4.0-interval database images show encouraging results for localizing both the inner and outer iris boundaries as circles shape with 100% accuracy rate.

References

Figure 11: Iris Segmentation.(a) original images from CASIA V4.0, (b) binary enhanced image and segmented pupil, (c) Enhanced image with inner and outer iris boundary detection. Table 1: Performance of iris segmentation for some methods which introduced by different researchers using CASIA v1.0 database. Iris Segmentation Guang-zhu XU et. al. [17] Weiqi Yuan et. al. [18] Muhammad Talal et. al. [19] Basit, A. et al. (20) Omran Safaa S. and Salih Maryam A. (21) Proposed Method

Accuracy rate 98.42% 99.45% 99.47% 99.6% 99.9%

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100%

[1] Wang Y., Y. Zhu, and T. Tan, “Biometrics Personal identification based on iris pattern,” Acta Automatic a Sinica, Vol.28, Pp.110, January 2002. [2] Ritu J., Gagandeep K., “Biometric Identification System Based On Iris, Palm And Fingerprints For Security Enhancement,” International Journal of Engineering Research & Technology, Vol.1, Pp. 1-4, 2012. [3] Daugman J. G., “How Iris Recognition Works,” Circuits and Systems for Video Technology, IEEE Transaction, Vol.14, Pp. 21-30, 2004. [4] Flom, L., and Safir, A., “Iris recognition system,” US patent 4,641,349, Patent and Trademark Office, Washington, D.C.,1987. [5] Daugman, J. G., “High confidence visual recognition of persons by a test of statistical independence,” Pattern Analysis and Machine Intelligence, IEEE Transaction on ISSN, Vol. 15, Pp. 1148–1161, 1993. [6] Nishino, Ko, and Nayar, Shree.K.,“Eyes for relighting,” ACM Trans. Graph, Vol. 23, No. 3, Pp. 704–711, 2004. Page 66

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 3, Issue 2, March – April 2014 ISSN 2278-6856 [7] Wildes, R.P., “Iris recognition: an emerging biometric technology,” Proceedings of the IEEE ISSN, Vol.85, Pp.1348–1363, 1997. [8] Masek L., “Recognition of human Iris patterns for biometric identification,” http://www.csse. uwa.edu.au/~pk/studentprojects/libor/, 2003. [9] Li Ma, Tieniu Tan, Yunhong Wang, Dexin Zhang, “Efficient iris recognition by characterizing key local variations,” IEEE Transaction on Image Processing, Vol.13, Pp.739-750, 2004. [10] Christel-Loc Tisse, Lionel Martin, Lionel Torres, Michel Robert, “Person identification technique using human iris recognition,” Proceedings of the 15th International Conference on Vision Interface, VI '02, Calgary, Canada, Pp. 294-299, 2002. [11] Jiali Cui, Yunhong Wang, Tieniu Tan, Li Ma, Sun, “An iris recognition algorithm using local extreme points,” Proceedings of the First International Conference on Biometrics Authentication, ICBA 04, Hong Kong, Pp. 442-449, 2004. [12] Lye Wil Liam, Chekima, A., Liau Chung Fan, and Dargham, J.A., “Iris recognition using selforganizing neural network,” IEEE, Student Conference on Research and Developing Systems, Malaysia, pp. 169–172, 2002. [13] Proenca H. and Alexandre L.A., “Iris segmentation methodology for non-cooperative recognition,” IEE Proc.Vis. Image Signal Processing, Vol. 153, No. 2, Pp.199-205, 2006. [14] Ghassan J. Mohammed, Bing-Rong Hong and Ann A. Jarjes, “Accurate pupil features extraction based on new projection function,” Computing and Informatics, Vol. 29, Pp. 663–680, 2009. [15] Chinese Academy of Science-Institute of Automation, CASIA Iris Image Database (Ver. 1.0), and available onhttp://biometrics.idealtest.org/dbDetailForUser.do? id=1. [16] Chinese Academy of Science-Institute of Automation, CASIA Iris Image Database (Ver. 4.0), and Available on: http://biometrics. idealtest.org/dbDetailForUser.do?id=4. [17] Guang-zhu XU, Zai-Feng ZHANG and Yi-de MA, “A novel and efficient method for iris automatic location,” Journal of China University of Mining and Technology, Vol. 17, Pp. 441–446, 2007. [18] Weiqi Yuan, Zhonghua Lin and Lu Xu, “A Rapid Iris Location Method Based on the Structure of Human Eyes,” Proceeding of the Annual International Conference-IEEE, Engineering in Medicine and Biology Society; Pp. 3020-3023, 2005. [19] Muhammad Talal Ibrahim, Tariq Mehmood, M. Aurangzeb Khan and Ling Guan, “A Noval and Efficient Feed Back Method for Pupil and Iris Localization,” Image Analysis and Recognition, 8th International Conference, ICIAR, Burnaby, BC, Canada, Proceedings, Part II, Vol. 6754, Pp. 79-88, 2011. Volume 3, Issue 2 March – April 2014

[20] Basit A., Javed M.Y. and Masood S.“Non-circular pupil localization in iris images,” International Conference on Emerging Technologies, IEEE-ICET, Rawalpindi, Pakistan, Pp. 228-231, 2008. [21] Omran Safaa S. and Salih Maryam A., “Iris Segmentation Using Staistical Measurements for the Intensity Values of the Eye Image,” International Conference on Information Technology, Pp.1, 2013. [22] Sreecholpech C. and Thainimit S., “A Robust Modelbased Iris Segmentation,” International Symposium on Intelligent Signal Processing and Communication Systems, (ISPACS), Pp. 599-602, 2009. [23] Hong-Lin Wan, Zhi-Cheng Li, Jian-Ping Qiao, BaoSheng Li, “Non-ideal iris segmentation using anisotropic diffusion,” The Institution of Engineering and Technology, IET Image Processing,Vol.7, Pp. 111-120, 2013. [24] Talebi S.M., Ayatollahi A., and Moosavi S.M.S., "A Novel Iris Segmentation Method based on Balloon Active Contour,” Iranian Conference on Machine Vision and Image Processing , Pp. 1-5, 2010. [25] Ann A. Jarjes, Kuanquan Wang, Ghassan J. Mohammed, “Iris Localization: Detecting Accurate Pupil contour and Localizing Limbus Boundary,” 2nd International Asia Conference on Informatics in Control, Automation and Robotics, Vol. 1, Pp. 349 – 352, 2010.

AUTHORS Iman A. Saad received the B.S. and M.S. degrees in computer science from Al-Mustansiriyah University, Iraq in 1993 and 2006, respectively. She is working as Lecturer, in Electronic Computer Center, AlMustansiriyah University, Baghdad, Iraq. She is currently pursuing the Ph.D. degree in computer science at Department of Mathematics, College of Science, Alepo University, Alepo, Syria. Dr. Loay Edwar George received the B.S. in Physics, M.S. in Theoretical Physics and Ph.D in Digital Image Processing degrees from Baghdad University, Iraq, in 1979, 1983 and 1997, respectively. He is a member of Arab Union of Physics and Mathematics, and the Iraqi Association for Computers. Now, he is the Head of Computer Science Department, Baghdad University.

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