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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) is an online Journal in English published bimonthly for scientists, Engineers and Research Scholars involved in computer science, Information Technology and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJETTCS are selected through rigid peer review to ensure originality, timeliness, relevance and readability. The aim of IJETTCS is to publish peer reviewed research and review articles in rapidly developing field of computer science engineering and technology. This journal is an online journal having full access to the research and review paper. The journal also seeks clearly written survey and review articles from experts in the field, to promote intuitive understanding of the state-of-the-art and application trends. The journal aims to cover the latest outstanding developments in the field of Computer Science and engineering Technology.
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: email@example.com, firstname.lastname@example.org Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 Acquisition of Iris Images, Iris Localization, Normalization, and Quality Enhancement for Personal Identification B.Sabarigiri1, T.Karthikeyan2 1 Research Scholar, Department of Computer Science, PSG College of Arts and Science, Coimbatore, India 2 Associate Professor, Department of Computer Science, PSG College of Arts and Science, Coimbatore, India Abstract: Iris based biometric personal identification and Section 2 deals with image processing steps that include Verification methods have gained much interest with an Iris Localization that comprises of Pupil, Iris Boundary increasing emphasis on security. The proposed approach Detection, Eyelash, and Eyelid Boundary Detection. comprises of acquisition of Iris images, Iris Localization, Normalization and Quality Enhancement. Algorithms like Circular Hough transform, Canny Edge Detection, Gabor filters, Homogeneous rubber sheet model, and Daubechies wavelets methods were used based on the requirements of the Iris Pre-Processing (IIP) Module. Accurate templates are the key to Iris recognition system. Artifacts Removal and Pre- Processing will help to produce accurate matching patterns. Our proposed work produced 99.14% accuracy in edge detection, which gives reliable solution to the segmentation and Quality Enhancement. Keywords: Iris Segmentation, Quality Enhancement, Circular Hough transform, Canny Edge Detection, Gabor filters, Homogeneous Rubber Sheet Model, Daubechies wavelets, IIP 1. INTRODUCTION Traditional methods for personal identification are based on what a person possesses (a physical key, ID card, etc.) or what a person knows (a secret password, etc). These methods have some problems, key may be lost, ID cards may be forged, and passwords may be forgotten. In recent years, bio metric personal identification is receiving growing interests from both academia and industry . There are two types of biometric features: physiological (e.g. Iris, face, fingerprint) and behavioral (e.g. voice and hand writing) Figure 1: Proposed Block Diagram for Iris Image Usually an iris image impossibly contains artifacts, Such Processing (IIP) Module as Pupil, eyelid, eyelash, etc. The artifacts may change After that Iris Normalization and Iris Image Quality and make the texture distort. It must eliminate the above Enhancement Finally, the conclusion and experimental said factors to the iris recognition through the image pre- results were proposed. processing. Mat lab tool used for its image manipulation and Pre- 2. ACQUISITION OF IRIS IMAGES processing. In this paper Section 1 deals with Acquisition The Iris recognition has been an essential research area for of Iris images and Iris image manipulation, Segmentation personal identification due to its high accuracy and the and Edge Detection. encouragement of both the government and private entities to replace traditional security systems, which suffer Volume 1, Issue 2 July-August 2012 Page 271 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: email@example.com, firstname.lastname@example.org Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 noticeable margin of error. Forty healthy volunteers (25 interest from the input image containing an eye. The IIP males and 15 females) IRIS images were collected. Module contains three major tasks. (i) Iris Localization Extraction of the IRIS image is more complicated, since (ii) Iris Normalization (iii) Iris Image Enhancement. IRIS is small in size and dark in Color. The IRIS patterns are differentiated by several characteristics including 3.1 Iris Localization ligaments, furrows, ridges, crypts, rings, corona, freckles, The Iris Localization Unit Contains Three Sections (i) and a Zigzag collarette. Stability is one of the key Pupil Detection and Iris Boundary Detection (ii) Eyelid advantages of IRIS recognition and it is suitable for one - Detection and (iii) Eyelash Detection many identification. Veri Eye Standard SDK used to extract IRIS Patterns in the Effective Manner. The image 3.1.1 Pupil Detection and Iris Boundary Detection size is 328×356 with 500 dpi and stored in the JPEG file Both the inner boundary and the outer boundary of a format. Each volunteer right eye image was collected and typical iris can be taken as circles. But the two circles are eight impressions on each have been made for IRIS usually not concentric. Compared with other part of the recognition. We took some efforts to control image quality eye, the pupil is much darker. The eyelids and eyelashes on eye pictures, and as well as appropriate settings such as normally occlude the upper and lower arts of the iris lighting and distance to camera were adjusted . region. Sometimes, specular reflections can occur within 2.1 Iris Image Manipulation the iris region corrupting the iris pattern. So we required to detect the above said artifacts, then only which will The images captured by camera have RGB color iris help to produce accurate matching patterns image. We transformed the images from RGB to gray (Templates). level and from eight-bit to double precision thus Hough Trans form is a standard computer algorithm that facilitating the manipulation of the images in subsequent can be used to determine the parameters of simple steps. geometric objects, Such as lines and circles in an image. The circular Hough Transform can be employed to 2.2 Iris Segmentation and Edge Detection deduce the radius and center coordinates of the pupil and Image can be viewed as depicting a scene composed of iris regions. different regions, objects etc. Then Image segmentation is An automatic segmentation algorithm based on the the process of decomposing the image into these regions Circular Hough Transform is employed by wildes et al, and objects by associating or labeling each pixel with the Kong and Zhang. Firstly, an edge map is generated by object that it corresponds to. Hence, segmentation sub calculating the first derivatives of intensity values in an divides an image into its constituent regions or objects. eye image and then thresholding the result. From the Before move in to the IIP Module we require to reduce the edge map, votes are cast in Hough space for the noise of the image using Gaussian Smoothing. It is parameters of circles passing through each edge point. replacing each pixel by the average of the neighboring These parameters are the center co-ordinates XC and YC, pixel values. Mathematically, 2-D Gaussian Function is and the radius r, which are able to define any circle written as according to define any circle according to the equation XC2+ YC2 = r2. 1 x ( n 1) x 2 y 2 A maximum point in Hough space will correspond to the g ( x, y ) e (1) 2 2 2 2 radius and center coordinates of the circle best defined by The Gaussian outputs a ‘weighted average’ of each the edge points Wiles et al  and Kong and Zhang  also make use of the parabolic Hough transform to detect pixel’s neighborhood, with the average weight more the eyelids, approximating the upper and lower eyelids towards the value of the central pixels. This is in contrast with parabolic arcs, which are represented as to the mean filter’s uniformly weighted average. Because of this, this, a Gaussian provides gentler smoothing and (-(x-hj)sin j+(y-kj)cos j)2=aj((x-hj)cos j+(y-kj) sin preserves edges better than a similarly sized mean filter. (2) Edge Detection refers to the process of identifying and Where aj controls the curvature, (hj,kj) is the peak of locating sharp discontinuities in an image. Variables parabola and j is the angle of rotation relative to the x- involved in the selection of an edge detection operator axis. includes Edge orientation, Noise Environment, Edge In performing the preceding edge detection step, Wiles et structure, There are many ways to perform edge detection al. bias the derivatives in the horizontal direction for They may be grouped in to two categories.(1) Gradient detecting the eyelids, and in the vertical direction for (2) Laplacian. detecting the outer circular boundary of the iris. The motivation for this is that the eyelids are usually 3. IRIS IMAGE PROCESSING horizontally aligned, and also the eyelid edge map will In the Iris image processing (IIP) module we using some corrupt the circular iris boundary edge map if using all image processing algorithms to demarcate the region of gradient data. Taking only the vertical gradients for locating the iris boundary will reduce influence of the Volume 1, Issue 2 July-August 2012 Page 272 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: email@example.com, firstname.lastname@example.org Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 eyelids when performing Circular Hough Transform, and To detect lower eyelid, the steps are repeated with lower not all of the edge pixels defining the circle are required IRIS boundary area. for successful localization. Not only does this make circle localization more accurate, it also makes it more efficient, 3.1.3 Eyelash Detection since there are less edge points to cast votes in the Hough Gabor filter and variance of intensity approaches are Space. proposed for eyelash detection. The eyelashes are categorized into separable eyelashes and multiple eyelashes. Separable eyelashes are detected using 1D Gabor filters. A low output value is obtained from the convolution of the separable eyelashes with the Gabor filter. For multiple eyelashes, the variance of intensity is very small. If the variance of intensity in a window is smaller than a Figure 2: (a)An eye image (b) Corresponding Edge map threshold, the center of the window is considered as the (c)Edge map with only horizontal Gradients (d) Edge eyelashes. map with only Vertical Gradients We can use Active Contour model has been used to localize Iris. The contour is defined as a set of n vertices connected as a simple closed curve. The movement of the contour is caused by internal and external forces acting on the vertices. The internal forces Expand the contour into a perfect circle. Figure 3: Pupil Detection Figure 4: Eyelash and Eyelid Detection The external forces push the contour inward. The average 3.2 Iris Normalization radius and center of the contour obtained are the Iris may be captured in different size with varying parameters of the Iris boundary. The discrete circular imaging distance. Due to illumination variations the active contour search for the Iris boundary is affected by radial size of the pupil may change accordingly. The the specular reflections from cornea. Therefore, image resulting deformation of the Iris texture will affect the preprocessing algorithm is required remove the specular performance of subsequent feature extraction and reflections matching stages. Therefore, the Iris region needs to be normalized to compensate for these variations. 3.1.2 Eyelid Detection The homogeneous rubber sheet model algorithm remaps Texture segmentation is adopted to detect upper and each pixel in the localized Iris region from the Cartesian lower eyelids in .The energy of high spectrum at each coordinates to polar co ordinate to polar co ordinates , region is computed to segment the eyelashes. The region . The non-concentric polar representation is with high frequency is considered as the eyelashes area. normalized to a fixed size rectangular block. The The information of the pupil position is used in upper homogenous rubber sheet model accounts for pupil eyelashes are fit with a parabolic arc. The parabolic arc dilation, imaging distance and non-concentric pupil shows the position of the upper eyelid. For eyelid displacement. This algorithm does not compensate for the detection, the histogram of the original image is used. rotation variance. The lower eyelid area is segmented to compute the edge point of the lower eyelid. The lower eyelid is fit with the Table 1: Accuracy of Iris Boundaries Detection edge points. In , the daubechies wavelets methods is used to decompose the original image in to four bands, Method Accuracy(%) HH,HL,LH and LL. Canny edge detection is applied to Hanho sung 98.55 the LH image. To minimize the influence of eyelashes, Xiaofu 97.67 canny edge detector is tuned to horizontal direction. The Cui.et.al 97.35 Canny Edge Detection Algorithm runs in 5 steps:(1) Md slim 90 Smoothing(2) Finding Gradients (3) Non maximum Mojtaba Najafi  98.64 suppression (4) Double thresholding (5) Edge tracking by hysteresis. Proposed System 99.14 The edge points that are close to each other are connected to detect the upper eyelid. The longest connected edge 3.3 Image Enhancement that fits with a parabolic arc is taken as the upper eyelid. Volume 1, Issue 2 July-August 2012 Page 273 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: email@example.com, firstname.lastname@example.org Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 The original image has low contrast and may have non Communication and Applications (ICCCA), uniform illumination caused by the position of the light 2012. source.  Ujwalla Gawande, Mukesh Zaveri, Avichal Kapur, “Improving Iris Recognition Accuracy by Score Based Fusion Method”, International Journal of Advancements in Technology, Vol 1, No 1, June 2010.  R.P Wiles, “Iris Recognition: An Emerging Biometric Technology”, Proceedings of the Figure 5: Iris Normalization and Teture Image after IEEE, Vol.85, No. 9, pp. 1348-1363, 1999. Enhancement  Wai-Kin-Kong & David Zhang, “Detecting Eyelash and Reflection for Accurate Iris These may impair the result of the texture analysis. We Segmentation” Proceedings of 2005 enhance the iris image and reduce the effect of non- International Symposium on Intelligent uniform illumination by means of local histogram Multimedia, Video and Speech Processing, equalization, Median filter and the 2-D Wiener filter. We Vol.8, pp.897-906,2005. just notify that the window size for using the median filter  Deepak Sharma, Ashok Kumar, “Iris is(3X3) and local variance are compared according to the Recognition-An Effective Human following equations: Identification”, International Journal of Computing and Business Research, volume 2 1 Issue 2, May 2011. a(n1, n 2) (3)  J.Cui,Y.Wang,T.Tan,L.Ma, and Z.Sun. “A Fast MN n1, n 2 and Robust Iris Localization Method Based on Texture Segmentation”, SPIE Defense and 2 1 2 Security Symposium, Vol.5404,pp.401-408, a 2 ( n1 , n 2 ) (4) 2004. MN n1, n 2  Y.Chen, S.Dass, and A.Jain “Localized IRIS Image Quality Using 2D Wavelets”, Proceedings Where refers to the local window with size MxN of International Conference on Biometrics,2006. around each pixel that is going to process, id the local  J.Daugman. “High confidance Visual Recognition 2 of persons by a test of statistical Independence”, mean value and is the local variance. The output of IEEE transactions Pattern Analysis nad machine wiener filter is obtained as follows: Intelligence, Vol.15,pp.1148-1161,1993. 2 2  J.Daugman “How IRIS Recognition works”, IEEE b(n , n ) ( a (n , n ) ) (5) Trans.CSVT, Vol.15,pp.21-30,2004. 1 2 1 2 2  Hanho sung jaekyung lim, “Iris Localization Where 2 presents the noise variance which is using collarette boundary Localization” 17th considered to be equal mean variance. International conference on pattern recognition, Cambridge, UK, Vol 4, pp.857-860, August 2004. 4. CONCLUSION  Xiaofu He, Pengfei shi “A new segmentation The algorithms which are used in this paper are already approach for iris recognition based on hand-held existing system but different combinations were produced capture device” Science direct, Pattern the Iris Segmentation accuracy. These steps will help to recognition,Vol 40,Issue 4,pp.1326-1333, April produce better Feature Extraction methods and matching 2007. patterns in the iris Recognition system. Actually, EEG  Richard N.F Yonh “An effective Segmentation will add as an additional modality to the IRIS .These two Methods for iris recognition System” 5th IEEE combinations will create high performance multimodal conference on visual information Engineering, biometrics in future. XianChina, pp.548-553, August 2008.  Md slim al. M, “Iris recognition: a new approach REFERENCES for iris segmentation”, 12th ICCIT, 2009.  Special Issues on Biometrics, Proceedings of the  Mojtaba Najafi, Sedigheh Ghofrani, “A new iris IEEE, vol.85, no.9, Sept 1997. identification Method Based on Ridgelet  T.Karthikeyan, B.Sabarigiri, “Countermeasures Tranform”, The 3rd International Conference on against Iris Spoofing and Liveness Detection Machine Vision(ICMV),2010. Using Electroencephalogram (EEG), International Conference on Computing, Volume 1, Issue 2 July-August 2012 Page 274 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: email@example.com, firstname.lastname@example.org Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 AUTHORS Sabarigiri.B received the M.C.A., M.Phil. Degree in Computer Science in 2007, 2010 receptively and is currently working towards the PhD degree in computer science at PSG College of Arts and Science, Coimbatore. He involved in the development of a multimodal biometric system which includes Iris and EEG. He has published 7 papers in national and international conferences and journals.. His areas of interest include Medical Image Processing, Biometrics, Fusion Techniques and Neuro Imaging. Thirunavu Karthikeyan received his graduate degree in Mathematics from Madras University in 1982. Post graduate degree in Applied Mathematics from Bharathidasan University in 1984. Received Ph.D., in Computer Science from Bharathiar University in 2009. Presently he is working as a Associate Professor in Computer Science Department of P.S.G. College of Arts and Science, Coimbatore. His research interests are Image Coding, Medical Image Processing and Data mining. He has published many papers in national and international conferences and journals. He has completed many funded projects with excellent comments. He has contributed as a program committee member for a number of international conferences. He is the review board member of various reputed journals. He is a board of studies member for various autonomous institutions and universities. Volume 1, Issue 2 July-August 2012 Page 275
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