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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 Human Iris Recognition In Unconstrained Environments Mohammad Ali Azimi Kashani Mohammad Reza Ramezanpoor Fini Department of Computer Science & Research Branch Department of Computer Science & Research Branch Islamic Azad University Branch Shoushtar Islamic Azad University Branch Shoushtar Shoushtar, Iran Shoushtar, Iran M.Azimi@iau-shoushtar.ac.ir MR.email@example.com Mahdi Mollaei Arani Department of Computer Science & Research Branch Payame Noor University Ardestan, Iran Dr.firstname.lastname@example.org Abstract—Designation of iris is one of biometric recognition methods .That use modal recognition technique and is base on II. AVAILABLE IRIS RECOGNITION SYSTEM pictures whit high equality of eye iris .Iris modals in comparison Daugman technique [3, 9] is one of oldest iris recognition whit other properties in biometrics system are more resistance system. These systems include all of iris recognition process: and credit .In this paper we use from fractals technique for iris Taking picture, assembling, coding tissue and adaption. recognition. Fractals are important in these aspects that can express complicated pictures with applying several simple codes. Until, That cause to iris tissue change from depart coordination A. Daugman techniques to polar coordination and adjust for light rates. While Daugman algorithm [3,9] is the famous iris algorithm. In performing other pre-process, fault rates will be less than EER, this algorithm, iris medaling by two circles than aren’t and lead to decreasing recognition time, account table cost and necessary certified. every circle defined whit there parameters grouping precise improvement. ( xo , y o , r ) that ( x o , y o ) are center of circle with r radios . Use - a differential – integral performer for estimating 3 Keywords-Biometrics; Identitydistinction;Identity erification; parameter in every circle bound. All pictures search rather to Iris modals. increasing r radius to maximize following Equation (1): I. INTRODUCTION I ( x, y ) G (r ) * ds Biometric use for identity distinction of input sample r x0 , y0 , r 2 r compare to one modal and in some case use for recognition In this formulate ( x , y ) is picture light intensify , ds is special people by determined properties .Using password or curve circle , 2 r use for normalization in tetras G( r ) is Gus identity card. Can create some problems like losing forgetting filter as used for flotation , and * is convolution performed thief. So using from biometric property for reason of special (agent). property will be effective. Biometric parameters dived to group base on figure one : Physiologic: this parameter is related to fig.1 of body human. Behavioral: this parameter is related to III. SUGGESTIVE ALGORITHM behavior of one person. In this algorithm, we use from new method for identity distinction base on fractal techniques, specially used fractal codes as coding iris tissue modal. For testing suggestive method, we used from available pictures in picture base of bath university. General steps of iris distinction would be as follow. Clearly indicate advantages, limitations and possible applications. Figure 1. grouping some biometrics property Figure 2. Sample of available pictures in iris database of Bath University 11 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 A. Iris assembling Then separated iris tissue control for light intensifies. The main goal of this part is recognition of iris area in eye Means picture contrast increased to iris tissue recognize very pictures. For this reason, we should recognize internal and good. In Fig.6 you can see sample of norm led iris tissue. external bound in iris by two circles. One method for assembling is using from fractal dimension. In Fig.3 we present dimension of Hough circle and in Fig. 4 show the Iris normalization, is more than 1 threshold. For accounting of fractal diminution, the picture dived to Blocks with 40 pixel width. As showed in picture, pupil and eye- lid areas recognized very good. Figure 6. Diagram of normal iris picture. C. Iris tissue coding In this step, we should coding iris tissue pixels set, and use it for comparing between 2 iris pictures. In suggestive methods. We use from fractal code. So fractal code of normal iris account. And this code as one modal saves in data base. To used for recognition and comparing iris pictures. In next step, we should encoding input picture with this fractal codes. So I need to change all pictures to standard size. For accounting fractal code first normal iris picture change to one rectangle 64*180 pixels. So fractal codes for different iris have same length.fig.7. Figure 3. output hough circle Figure 7. Normal iris picture in diminution 64*180 pixels D. Change range to wide blocks Main step in accounting fractal picture coding is changing range to wide blocks. For every wide block copy of range Figure 4. Iris normalization block compare to that block. W changing is combination of geometrics and light changing. In case of I grey picture, if z B. Iris normalization express pixel light intensify in (x, y), we can show w as matrix as follow: In this step, should decant coordination change to polar coordination. For this reason , 128 perfect circle next to pupil center and with starting from pupil radius toward out , separate x a b 0 x e from iris , pour pixels on these circles in one rectangle , in this W y c d 0 y f way iris that was the form of circle trope , change to rectangle, z 0 0 s z o it means iris from Decoct coordination change to polar coordination. In fig.5 you can watch iris polar coordination. f, a, b, c, d, e coefficient, control main aspect of changing Since changing in light level, pupil environment of iris geometrical. While s, o recognized contrast and both of them changed. We should control input light. However, it may recognize light parameters (fig.8). Changing geometrics person interval different from camera, but size of iris doesn’t parameters limit to hardness adaption.  same in different pictures. So with choosing this 128 prefect circles iris normalization done in respect to size. Figure 5. Diagram of polar coordination of iris tissue Figure 8. Picture of rang and wide blocks 12 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 Comparing range wide in a 3 steps process. One of base eight directions applied on selected range block. Then, oriented D ( x‚y) max 0 i N xi yi range block, become minimum till be equal to wide block Rk. If we want general changed be contradictor, wide should be range block . However, present ting picture as set of F. Suggestive method simulation changed blocks, don't present precise copy, but it´s good Suggestive method for identity recognition performed on approximate. Minimizing fault between Rk and w (Dj) can subset iris picture data base in Bath University. Available minimize fault between estimated and main picture. If ri and d subset include 1000 picture from 25 different persons. 20 and I=1‚…‚n be pixel amounts relate to blocks having same pictures from left eye and 20 picture form right eye were size Rk and shrink , fault and ERR is as following : showed. Since iris left and right eye is different in every person. Among every 50 eyes, from 20 pictures, 6 pictures are n considered for teaching and testing (fig.9.10.11). Err ( s .d i o ri ) 2 i 1 n Err n.o 2 (s 2.d i2 2.s.d i.o 2.s.d i.ri 2.o.ri ri2) i 1 n err ( 2.s.di2 2.di. .o 2.di .r ) i 0 s i 1 n n Err 2.n.o ( 2.n.o ( 2.s.di 2.r ) i 0 i 1 i 1 It happens when : n n n n d .r d r i 1 i i i 1 i i 1 i s n n n di 2 ( d )2 i 1 i 1 i 1 n n o ri s d n i i 1 i One of advantage of suggestive method for iris recognition Figure 9. curve ROC relate to suggestive identity verification system. is that when registering person, we save input fractal code of person iris picture as modal in data base, and so with regard to compressing property of fractal codes, we have less weight data base. E. Grouping and adapting In this respect we should compare input picture with available modals in data base system, and achieve similarity between them. For this reason, iris norm led picture encoding with available fractal codes in data base. For recognition similarity between input and encoding picture, used form interval between them. Nominal similarity size is 0 and 1 . Interval form mincosci defined base on soft LP: N 1 d p (x‚y) p (xi yi ) p i 0 Figure 10. curve ROC RELATES to suggestive identity verification system When p , achieved L : with regard to adoptions numbers. (n= 1, 2, 3, 4, 5) 13 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 and sub fractal techniques. Also for more precise identity distinction and adaptation use more various grouping techniques like k (nearest neighborhood). REFERENCES  A. K. Jain, R. Bole, S. Penchant, “Biometrics: Personal Identification in Network Society.” Kluwer Academic Publishers, 1999.  A. K. Jain, A. Ross and S. Pankanti, "Biometrics: A Tool for Information Security" IEEE Transactions on Information Forensics and Security 1st (2), 2006.  International Biometric Group, Independent Testing of Iris Recognition Technology, May 2005.  J. Daugman, “How iris recognition works”, IEEE Trans. Circuits Systems Video Technol. v14i1. 21-30, 2004.  J. Daugman, “High confidence visual recognition of persons by a test of statistical independence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, pp.1148-1161, 1993. Figure 11. curve ROC relate to suggestive identity verification system with  J. Daugman, “The importance of being random: Statistical principles of regard to adoptions numbers. iris recognition” Pattern Recognition 36, 279–291, 2003.  J.Daugman, “New Methods in Iris Recognition”.IEEE TRANSACTIONS ON SYSTEMS, MAN, AND YBERNETICS, 2007. TABLE I. COMPARING IDENTITY DISTINCTION PRECISE OF SUGGESTIVE  J. Daugman, “Demodulation by complex-valued wavelets for stochastic SYSTEM WITH DAUGMAN METHOD BASE ON REGISTER TEACHING PICTURE NUMBER . (N=1, 2, 3, 4, 5, 6) pattern recognition” International Journal of Wavelets, Multiresolution and Information Processing, 1(1):1–17, 2003. Identity Daugman Identity suggestive Picture  J. Daugman, “New Methods in Iris Recognition”. IEEE method method number(n) TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, %96 %88 1 picture 2007. %96 %86 2 picture  H. Ebrahimpour-Komleh, Fractal Techniques for Face Recognition, PhD %96 %94 3 picture thesis, Queensland University of technology, 2004. %96 %94 4 picture  H. Ebrahimpour-Komleh, V. Chandra., and S. Sridharan, "Face recognition using fractal codes" Proceedings of International Conference %96 %96 5 picture on Image Processing(ICIP), vol. 3, pp. 58-61, 2009. %96 %96.13 6 picture  H. Ebrahimpour-Komleh, V. Chandra, and S. Sridhar an, "Robustness to expression variations in fractal-based face recognition" Sixth International, Symposium on Signal Processing and its Applications, vol. 1, pp. 359-362, 2001. IV. CONCLUSION  H. Ebrahimpour-Komleh, V. Chandra, and S. Sridhar an “An In this paper, we have proposed a new method base on Application of Fractal Image-set Coding in Facial Recognition,” fractal techniques for identity verification and recognition with Springer Verlag Lecture Notes in computer science, Volt 3072, help of eye iris modals. For a lot of reasons that iris modals Biometric authentication, pp178-186, Springer-Velar, 2004. have ran the than other biometrics properties it’s more fantastic. In assembling part. It says that with using of light in Mohammad A. Azimi Kashani (Jun ’83) tensely process techniques and modeling performance and received the B.S and M.S degrees in computer Anny margin or can recognize iris internal bound. In engineering from Islamic Azad university of normalization part centrifuged rules toward pupil center and Kashan and Dezfoul, Iran in 2006 and 2009 starting radius toward out, can determine noise originate from respectively. He works in the area of PCA and eye-lash and eye-lid. Since in coding and encoding iris picture his primary interest are in the theory of detection and estimation, including face and we use fractal codes iris fractal codes save as modals in detection, eye detection, face and eye tracking. data base. This method has same advantages like less weight of He accepted numerous papers on different database .more security and relative good precise. when conferences for IEEE. entering one person, iris picture encoding on fractal codes for one step, to Euclid interval and interval minimum e method can use .In suggestive system normalization part, iris tissue change Mohammad R. Ramezanpour fini (sep’85) form depart coordination to polar coordination and adjust light received the B.S degree in computer in tensely, while performing other preprocess, fault rate ERR engineering from Islamic Azad university of will be less than this amount .If used data base in iris Kashan , Iran, in 2007 and the M.S degree distinction system be big, search time will be a lot. So, in from the Islamic Azad university of Arak, grouping and adapting iris modals for reason of decreasing Iran, in 2010. His research interests are distinction time, decreasing accounting cost and improving primarily in the fields of communication, grouping precise, can use form diminution fractal. Also, it is image processing and signal processing. suggest using fractal codes as iris tissue property and using Presently, he is working in image processing, coding techniques fractal picture set for confine fractal codes cam-shift, particle filter and Kalman filter for estimate and tracking. 14 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
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