A Hybrid method for Iris Segmentation

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A Hybrid method for Iris SegmentationMuaadh Sh. Azzubeiry and Dzulkifli Bin Mohammed

Abstract — Advance development in security technology has caused many major corporations and governments to start

employing modern techniques in identifying the identity of the individuals. Among the common biometric identification methods

are facial recognition, fingerprint recognition, speaker verification and so on, presenting a new solution for applications that

require a high degree of security. Among these biometric methods, iris recognition becomes an important topic in pattern

recognition and it depends on the iris which is located in a place that is still stable through human life. Furthermore, the

probability to find two identical irises approaching to zero value is quite easy. The identification system consists of several

stages, and segmentation is the most crucial step. The current segmentation methods still have its limitation in localizing the iris

due to the circular shape consideration of the pupil. In this paper an enhanced hybrid method, which can guarantee the

accuracy of the iris identification system is proposed. The proposed method takes into account the elliptical shape of the pupil

and iris. Moreover, Eyelid detection is another step that has been proposed in this paper as a part of segmentation stage. The

dataset which is used is CASIA v3 including the three subsets: Interval, Lamp and Twin. The performance measurement of the

proposed method is done by determining the number of success images. The results of the study are very promising with an

accuracy of 99.1% as compared to the related existing methods.

Index Terms — Eyelid detection, Iris segmentation, Pupil detection

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1 INTRODUCTION

he iris is known as the thin colored area, which islocated between the cornea and the lens of the hu-man's eye. Its center is closed by a part known as the

pupil [1]. If we make a comparison among biometrics, irisidentification systems will gain a good result. Iris texturehas a very high degree of freedom and that make it ex-

tremely important. What's more, the chance of findingtwo persons who have the same identical irises is close tozero and most iris patterns remain stable over one’s lifetime. Therefore, the iris identification system is the mostreliable among other identification systems and for sure itcan be useful in many secure places [2]. A standard irisidentification system consists of four stages: acquisition,preprocessing, feature extraction and matching [3]. Pre-processing combines three steps: segmentation, normali-zation and enhancement. Acquisition is the process ofgetting the image from the source by using a particularlydesigned device for this purpose. Preprocessing is used toenhance the captured image and prepare it for the nextstage which is feature extraction. Eventually, matching isperformed to check whether the current features thathave been extracted have a match with the existing fea-tures of candidate iris to identify the identical iris [4].

Iris segmentation is the most important and seriousstep in the iris identification system. It is to localize theexact iris area image from the human eye image. The out-put of this stage is very important and has played a pri-

mary role in the steps after segmentation (normalization,enhancement, feature extraction and matching). The effi-ciency of iris identification system is primarily dependenton the accurate output of iris segmentation [5].

The iris segmentation’s role is to identify the iris regionin the eye image (fitting detection for iris boundaries); we

can see that in figure 1 below.

Figure 1: An iris segmentation stage

Therefore, in this paper we will concentrate on irissegmentation stage to increase the performance of irisrecognition system. In addition, an enhanced method toguarantee the accuracy will be applied to increase theaccuracy of the identification system.

2 RELATED WORK

Iris segmentation stage is the most important stage thatcan be the origin of the accuracy of the iris. Based on thisfact a lot of researchers exerted their efforts trying to finda suitable algorithm to achieve the goal of including thewhole iris. That was because missing to include any part

will eventually affect the efficiency of the iris identifica-tion system.In [6, 7, 8] proposed the Integro-differential operator(IDO), which actually treated the pupil and limbus of the

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• Muaadh Sh. Azzubeiry , Faculty of Computer Science and InformationSystems Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.

• Dzulkifli bin Mohamad , Faculty of Computer Science and InformationSystems Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.

T

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 9, SEPTEMBER 2011, ISSN 2151-9617

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iris as a circular shape and trying to detect these bounda-ries by yielding a search for large circular variations in theimage. An integro-differential operator (IDO) was usedfor locating the inner and outer boundaries of an iris bymeans of the following optimization:

where I (x, y) is the image that contains the eye. The IDOseek over the image domain (x, y) for the maximum in thepartial derivative that is blurred due to to increasing ra-dius r, of the integral contour of I (x, y) that has beennormalized along a circular arc ds of radius r and centercoordinates (x0, y0). The symbol ∗ denotes convolutionand Gσ (r) is a smoothing function such as a Gaussian of

scale σ. The common behavior of the IDO is actually acircular edge detector. The IDO searches for the gradientmaxima over the 3D parameter space, so there are nothreshold parameters required as in the Canny edge de-tector. By changing the contour path from circular to anaccurate design, IDO also detects the upper and lowereyelid boundaries. The integro-differential can be consi-dered as a modified version of the Hough transform,since it also makes use of the image's first derivatives andcarry out a search to find geometric parameters. Since itworks with raw derivative information, it does not sufferfrom the threshold problems of the Hough transform.However, the algorithm can fail where there is noise in

the eye image, such as from reflections, since it worksonly on a local scale. A method introduced by [9] to seg-ment iris which was based on rotation average analysis ofintensity-inversed image. The method segmented the in-ner boundary and fitted the outer boundary (both as acircle) by least-square non-linear circular regression. As[10] stated an algorithm to detect the boundaries betweenboth pupil and iris and sclera and iris. A technique calledrectangular area is applied in order to find the pupil anddetect the inner circle of iris and after that detect the irisouter boundary.A Circle Density Based Iris Segmentation method (CDIS)

has been proposed by [11]. It consists of specular reflec-tion deduction, eyelash elimination and Iris segmentationalong with eyelid removal based on the local image statis-tics and block intensity. In addition, [12] proposed a me-thod based on the adaptive boosting eye detection algo-rithm (AdaBoost), which is an algorithm that constructs astrong classifier by coupling the weak classifiers), in orderto balance the iris detection errors caused by the two cir-cular edge detection operations. On the other hand, [13]introduced a new method to segment iris which wasbased on rotation average analysis of intensity-inversedimage in detail. The inner boundary is segmented and the

outer boundary is fitted (both as a circle) by least-squarenon-linear circular regression. [14] Proposed an algorithmfor detecting the boundaries between pupil and iris andalso sclera and iris. The rectangular area method was ap-plied in order to segment the pupil and detect the inner

and outer boundary of iris.

3 INSPIRATION

Most iris segmentation techniques assume the boundaryof pupil and iris to be as a circle, but their boundary is notquite like a circle and a small error in detecting the boun-dary of the iris will lead to lose some information thatexists around the iris. Furthermore, there are a lot of chal-lenges that should be overcome by proposing an en-hanced segmentation method that can yield precise re-sults as input to the identification system to enhance itsefficiency.

4 PROPOSED IRIS SEGMENTATION METHOD

An eye image contains not only the iris region but alsocontains some unwanted parts like sclera, pupil and eye-

lids. Moreover, the pupil boundary can be considered as

the inner boundary of the iris. For this reason, the pro-

posed method goes through the following steps:

4.1 Pupil Detection

In this step, we will detect the pupil in an iris image,taking into account the elliptical shape of the pupil. To dothat first, we binarize the image then find the linear indic-es of the resulting entry image after removing the noise.After that, fit the values that have been found as ellipse.

The center of the ellipse will be the intersection of the twolines which have been drawn from the min point to themax point in X- axis with the line from the min value tothe max value of Y-axis. Direct Least Square fitting of el-lipse which has been proposed by [15] was applied.

Original Image

1-Binary Image 2-Removing Noise

3-Edge Detection 4-Pupil Detection

Figure 2: Pupil Detection Steps







4.2 Iris localization

In this step, we can detect the boundary of the iris basedon its inner boundary. The same previous method of de-tecting the pupil is applied by considering the diameter of

iris and the center of the pupil.

4.3 Eyelid Detection

To determine the edge of the eyelids, the following steps

were followed:

1. Extract edge of eyelid using Canny (define thre-shold value of Canny).

2. Remove unwanted eyelashes and another edge

(define threshold value of noise).3. Divide the eyes into 4 regions based on the centerpoint of the pupil.

4. Tracing pixels in each region to find the eyelid.5. Save the 4 points of the eyelid [(x, y) top and (x, y)

bottom].6. Connect each corresponding point to draw the

curve/arc.

Figure 4: Eye regions determination

We should check pixel by pixel in each region of the

determined regions that have been mentioned, if the pixelis foreground (eyelid edge), the pixel it will record the x,y of that pixel. From figure 5 below, it can be seen that thegreen line is our border for each region. The number ineach row, the column is how the pixel is traced. In thetop-left side (red) region, tracing starts from 1 (see greenline) then move to the upper row (2) then move again toupper row (3) until it reach the image border (4) it willstart again but in the different column (indicated bynumber 5). Tracing will be stopped if foreground pixel(eyelid edge) is found.

4 8 12 8 4

3 7 11 7 3

2 6 10 6 2

1 5 9 5 1

2 6 10 6 2

3 7 11 7 3

4 8 12 8 4

Figure 5: Searching technique for eyelid point

The determined points that hae been found, will looklike the following figure

After determining the points, an arc should be drawnfor each eyelid as shown in figure 7.

Figure 3: Iris Localization

Figure 6: Determined points in a binarized image of the eye

Figure 7: Eyelid Detection

Figure 3: Iris Localization

Top left Top right

Bottom left Bottom right







5 EXPERIMENTAL RESULTS

The proposed method was applied using 3060 imagesincluding 200 irises taken from CASIA v.3 [17, also in-cluding the three subsets. All the steps were carried outusing MATLAB on Fujitsu Siemens Laptop, Intel (R)Core(TM) 2 CPU T5600 @ 1.83 GHz, 1 GB RAM, Windows

XP Professional Service Pack 3. The results of the pro-posed method were: iris localization on the interval sub-set was 99.9%. The same result also was for both subset(Lamp and Twin). Elliptical consideration of the pupil inthe segmentation step has played a significant role byincluding the whole pupil area and then including thewhole iris. On the other hand, eyelid detection resultswere 99.9% on Interval subset while the results were 98%on Lamp and 97% on Twin. The challenge that has beenfaced was on Twin subset and the difficulty was in find-ing the right points that should be determined to drawthe arc due to the complex borders after extraction. The

final results of the proposed segmentation method (irislocalization and eyelid detection) with a comparisonamong some existing methods are presented in Table 1.

The results were obtained by calculating the number ofimages that have been segmented relative to the accuratetotal number of images.

Figure 8,9,10 show the results of segmentation by us-ing either method that considers the pupil as a circle ormethods that consider the pupil as an ellipse. Hence, itwas concluded that the resulted images of the ellipticalshape consideration is better than those which resulted byusing circular shape consideration.

A B

Figure 8: Segmentation results with A) Circular shape consider-

ation B) Elliptical shape consideration for Interval subset.

A B

Figure 9: Segmentation results with A) Circular shape considera-tion B) Elliptical shape consideration For Lamp subset.

A B

Figure 10: Segmentation results with A) Circular shape consideration B)Elliptical shape consideration For Twin subset.

6 CONCLUSION

In this paper, we enhanced a method that can guarantee theaccuracy during the segmentation stage to include the wholeiris image by considering the elliptical shape of the pupil andthe iris. The proposed method includes pupil detection, irisocalization and eyelid detection. Most techniques that havesbeen used were based on [1] and [11] techniques.

7 REFERENCES

[1] Daugman, J.G. (2004), how iris recognition works, IEEE Trans.

Circ. Syst. Video Technol, Vol: 14, pp.21–30.

[2] Park, H.A. and Park K.R. (2007), Iris recognition based on score

level fusion by using SVM, Pattern Recognition Letters Vol:28

(15), pp.2019–2028.

[3] Miyazawa, K., Ito K., Aoki T., Kobayashi K. and Nakajima H.

(2008), An effective approach for iris recognition using phase-

based image matching, IEEE Transactions on Pattern Analysis

and Machine Intelligence, Vol:30 (10), pp. 1741–1756,

[4] Yu, L., Zhang, D. and Wang K. (2007), The relative distance of

key point based iris recognition, Pattern Recognition ,Vol:40(2),pp. 423–430.

[5] Kim , J., Cho S. and Choi J. (2004), Iris recognition using wave-

let features, Journal of VLSI Signal Processing,Vol:38 (2) ,

TABLE 1THE PERFORMANCE OF THE PROPOSED METHOD AMONG SOME

EXISTING METHODS

Method Iris Segmentation Accuracy

Interval Lamp Twin

Proposed99.9% 98.95% 98.45%

Gupta [11]99.9% 81% 80%

Masek Libor [16]83% 82% 81%







pp.147–156.

[6] Daugman, J.G (1993), "High Confidence Visual Recognition of

Persons by a Test of Statistical Independence", IEEE Transac-

tions On Pattern Analysis and Machine Intelligence, Vol. 15,

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[7] Daugman, J.G (2001), “Statistical Richness of Visual Phase In-

formation: Update on Recognizing Persons by Iris Patterns,” In-

ternational Journal of Computer Vision, vol:45, no. 1, pp. 25–38.

[8] Daugman, J.: New methods in iris recognition. IEEE Transac-

tions on Systems, Man, and Cybernetics-Part B 37(5), 1167–1175

(2007)

[9] Wei, Li and Lin-Hua Jiang (2009), Fast Iris Segmentation by

Rotation Average Analysis of Intensity-Inversed Image, Sprin-

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[10] Rahib Hidayat Abiyev and Koray Altunkaya (2009), Neural

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[11] Gupta, Anand, Anita Kumari, Boris Kundu, and Isha Agarwal(2009),CDIS: Circle Density Based Iris Segmentation. Second In-

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[12] Dae, Sik Jeong, Jae Won Hwang, Byung Jun Kang, Kang

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(2010). A new iris segmentation method for non-ideal iris im-

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[13] Wei, Li and Lin-Hua Jiang (2009), Fast Iris Segmentation by

Rotation Average Analysis of Intensity-Inversed Image , Sprin-

ger Berlin / Heidelberg Vol: 5855, pp. 340-349.

[14] Rahib Hidayat Abiyev and Koray Altunkaya (2009), Neural

Network Based Biometric Personal Identification with Fast Iris

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[15] Fitzgibbon , A., Pilu M., and Fisher R. B., “Direct least square

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[16] IEEE Trans. Pattern Anal. Machine Intell., vol. 21, pp. 477–480,

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[17] Masek L. and Kovesi P. (2003), MATLAB Source Code for a

Biometric Identification System Based on Iris Patterns. The

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[18] CASIA iris database, Institute of Automation, Chinese Acade-

my of Sciences, http://sinobiometrics.com/casiairis.h.

Muaadh Sh. Azzubeiry is a Ph.D. student in Computer Science andInformation Systems Faculty at the Universiti Teknologi Malaysia,Malaysia. He received his Bachelor of Science in Computer Sciencein 2002 at Almustansiriah University, Baghdad, Iraq and a Master ofScience in Computer Science in 2010 from the University of Tech-nology Malaysia, Malaysia -UTM. In 2011, he started his Ph.D. inComputer Science at the Department of Computer Graphics andMultimedia, UTM. His research interests include pattern recognitionand biometrics and security systems (fingerprint classification andrecognition, signature verification, iris identification and face recogni-tion). He is till date a lecturer at Sana’a Community College 2002,

Yemen.

Prof. Dr. Dzulkifli bin Mohamad is now a Professor at the Universi-ty of Technology Malaysia. He received his Bachelor of Science from

National University of Malaysia in 1978, a Postgraduate Diplomafrom the University of Glasgow, UK in 1981, a Master of Sciencefrom the University of Technology Malaysia in 1990 and Ph.D. fromthe University of Technology Malaysia in 1997. He held differentpositions in UTM. He is a consultant for different firms. He super-vised more than 120 master and Ph.D. students. Furthermore, heevaluated/examined more than 200 post-graduates. Prof. Dr. Dzulkif-li has received numerous awards and published more than 200 re-

search papers in the international journals and conferences. Hisareas of interest are biometrics, pattern recognition, multimedia sig-nal processing.





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A Hybrid method for Iris Segmentation