IRIS RECOGNITION USING IMAGE REGISTRATION TECHNIQUE BY NORMALIZED MAPPING FUNCTIONS

Size: px

Start display at page:

Download "IRIS RECOGNITION USING IMAGE REGISTRATION TECHNIQUE BY NORMALIZED MAPPING FUNCTIONS"

Arline Bailey
5 years ago
Views:

been a fast growing, challenging and interesting area in real-time applications. The verification of IRIS recognition is one of the most reliable personal identification methods in Biometrics.

1 Research Article IRIS RECOGNITION USING IMAGE REGISTRATION TECHNIQUE BY NORMALIZED MAPPING FUNCTIONS *1 S.Nithyanandam, 2 K.S.Gayathri, 2 R.Prabhudoss Address for Correspondence 1 Research Scholar, Prist University, Thanjavur, India Assistant Professor, Prist University, Thanjavur, India ABSTRACT IRIS recognition has been a fast growing, challenging and interesting area in real-time applications. The verification of IRIS recognition is one of the most reliable personal identification methods in Biometrics. A large number of IRIS recognition algorithms have been developed for decades. In this paper, a Canny Edge Detection Scheme and a Circular Hough transform used to detect the IRIS boundaries in the eye s digital image. The extracted IRIS region was then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to four levels to encode the unique pattern of the IRIS into a bit-wise biometric template. Then, comparing is done by using the hamming distance matching algorithm, which determines whether two IRIS s are similar. Experiments on CASIA IRIS image database shows that the IRIS recognition method based on mapping function is reliable and quite effective. KEYWORDS IRIS recognition, normalization, Feature encoding, Matching, Circular Hough transform, CASIA iris database. I.INTRODUCTION The human iris recently has attracted the attention of biometrics-based identification and verification research and development community. The iris is so unique that no two irises are alike, even among identical twins, in the entire human population. The term iris recognition refers to identifying, an iris image by computational algorithms and it is used as a identity. Iris recognition technology offers the highest accuracy in identifying individuals as compared to any other method available. This is because no two irises are alike, not between identical twins, or even between the left and right eye of the same person. Irises are also stable, unlike other identifying characteristics that can change with age, the pattern of one's iris is fully formed by ten months of age and remains the same for the duration of their lifetime. A key advantage of iris recognition is its stability, or template longevity, as, barring trauma, a single enrolment can last a lifetime. Because of its speed of comparison, iris recognition is the only biometric technology well-suited for one-to-many identification. The system is to be composed of a number of subsystems, which correspond to each stage of iris recognition. These stages are segmentation, which locates the iris region in an eye image, normalization, which creates a dimensionally consistent representation of the iris region, and feature encoding creates a template containing only the most discriminating features of the iris. The input to the system will be an eye image, and the output will be an iris template, which will provide a mathematical representation of the iris region. Figure 1. Iris Image II.IMAGE PREPROCESSING Image pre-processing and normalization is significant part of iris recognition systems. 1) Iris Segmentation. This involves first employing Canny Edge Detection to generate an edge map. 2) Iris Localization. In the work, in order to increase the overall speed of the system, circle houghman detection algorithm is used.

The eyelids and eyelashes normally occlude the upper and lower parts of the iris region. A technique is required to isolate and exclude these artifacts as well as locating the circular iris region.

2 1.Segmentation The first stage of iris recognition is to isolate the actual iris region in a digital eye image. The iris region can be approximated by two circles, one for the iris/sclera boundary and another, interior to the first, for the iris/pupil boundary. The eyelids and eyelashes normally occlude the upper and lower parts of the iris region. A technique is required to isolate and exclude these artifacts as well as locating the circular iris region. For that purpose circular hough transform and canny edge detection technique is used. Circular Hough transform for detecting the iris and pupil boundaries. This involves first employing Canny edge detection to generate an edge map. Canny Edge Detection Scheme Canny edge detection is used to create an edge map, and only horizontal gradient information is taken. The boundary of the iris is located by using canny edge detection technique. From the edge map, votes are cast in Hough space for the parameters of circles passing through each edge point. These parameters are the centre coordinates x and y, and the radius r, which are able to define any circle according to the equation, x 2 + y 2 =r 2 (1) In performing the preceding edge detection step, the derivatives in the horizontal direction is for detecting the eyelids, and in the vertical direction for detecting the outer circular boundary of the iris. 2.Localization Firstly, it finds the sketchy pupil center through the gray projection and the pupil center detection operator. Secondly, finds four iris inner boundary points by the direction edge detection operator and the voting mechanism beginning from the sketchy pupil center, and locates the iris inner boundary according to these four points. Finally finds four iris outer boundary points by the direction edge detection operator and the voting mechanism beginning from the center of pupil, and locates the iris outer boundary according to these four points. Localization accuracy rate of this method is high, the speed is quick. Circular Hough Transform The Hough transform is a feature extraction technique used in image analysis. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure. This voting procedure is carried out in a parameter space, from which object candidates are obtained as local maxima in a so-called accumulator space. That is explicitly constructed by the algorithm for computing the Hough transform. Figure 2. Detection of circular boundaries of pupil and iris. The parabolic Hough transforms to detect the eyelids, approximating the upper and lower eyelids with parabolic arcs, which are represented as, (-(x-h j )sin θ j +(y-k j )cos θ j ) 2 = (a j (x-h j )cos θ j +(y-k j )sin θ j ) (2) Where, a j controls the curvature,(h j, k j ) is the peak of the parabola and θ j is the angle of rotation relative to the x-axis. III.NORMALIZATION After locating the iris, it cannot carry on the code for the locating iris image immediately, and should carry on the calibration firstly. Therefore it should adjust each primitive image to the same size and corresponding position through normalization. Figure3. Frame work of iris recognition Image Registration

The system employs an image registration technique, which geometrically warps a newly acquired image, I a (x,y) into alignment with a selected database image I d (x,y).

3 The system employs an image registration technique, which geometrically warps a newly acquired image, I a (x,y) into alignment with a selected database image I d (x,y). When choosing a mapping function (u(x,y),v(x,y)) to transform the original coordinates, the image intensity values of the new image are made to be close to those of corresponding points in the reference mage. The mapping function must be chosen so as to minimize, (I d (x,y) - I a (x-u,y-v)) 2 dxdy (3) while being constrained to capture a similarity transformation of image coordinates (x,y) to (x,y ) that is, (4) with s as a scaling factor and R(Φ) is a matrix representing rotation by Φ. In implementation, given a pair of iris images I a and I d, the warping parameters s and φ are recovered via an iterative minimization procedure Figure 4. Normalized iris image IV.ENCODING AND MATCHING The template that is generated in the feature encoding process will also need a corresponding matching metric, which gives a measure of similarity between two iris templates. This metric should give one range of values when comparing templates generated from the same eye, known as intra-class comparisons, and another range of values when comparing templates created from different irises, known as inter-class comparisons. These two cases should give distinct and separate values, so that a decision can be made with high confidence as to whether two templates are from the same iris, or from two different irises. Gabor Filters Gabor filters are able to provide optimum conjoint representation of a signal in space and spatial frequency. A Gabor filter is constructed by modulating a sine/cosine wave with a Gaussian. This is able to provide the optimum conjoint localization in both space and frequency, since a sine wave is perfectly localized in frequency, but not localized in space. Modulation of the sine with a Gaussian provides localization in space, though with loss of localization in frequency. Decomposition of a signal is accomplished using a quadrature pair of Gabor filters, with a real part specified by a cosine modulated by a Gaussian, and an imaginary part specified by a sine modulated by a Gaussian. The real and imaginary filters are also known as the even symmetric and odd symmetric components respectively. The centre frequency of the filter is specified by the frequency of the sine/cosine wave, and the bandwidth of the filter is specified by the width of the Gaussian. Daugman makes uses of a 2D version of Gabor filters in order to encode iris pattern data. A 2D Gabor filter over the an image domain (x,y) is represented as, (-π [(x-x 0 ) 2 /α 2 =(y-y 0 ) 2 /β 2 ] -2πi[u 0 (x-x 0 )+v 0 (y-y 0 )] G(x,y) = e (5) where, (x 0,y 0 ) specify position in the image, (α,β) specify the effective width and length, and(u 0,v 0 ) specify modulation, which has spatial frequency t(u v 0 2 ). Hamming Distance The Hamming distance gives a measure of how many bits are the same between two bit patterns. Using the Hamming distance of two bit patterns, a decision can be made as to whether the two patterns were generated from different irises or from the same one. In comparing the bit patterns X and Y, the Hamming distance (HD), is defined as the sum of disagreeing bits (sum of the exclusive-or between X and Y) over N, the total number of bits in the bit pattern. The derived equation for HD is, 1 N HD = X j (XOR) Y j (6) N j = 1

Since an individual iris region contains features with high degrees of freedom, each iris region will produce a bit-pattern which is independent to that produced by another iris, on the other hand,

4 Since an individual iris region contains features with high degrees of freedom, each iris region will produce a bit-pattern which is independent to that produced by another iris, on the other hand, two iris codes produced from the same iris will be highly correlated. If two bits patterns are completely independent, such as iris templates generated from different irises, the Hamming distance between the two patterns should equal 0.5. This occurs because independence implies the two bit patterns will be totally random, so there is 0.5 chance of setting any bit to 1, and vice versa. Therefore, half of the bits will agree and half will disagree between the two patterns. If two patterns are derived from the same iris, the Hamming distance between them will be close to 0.0, since they are highly correlated and the bits should agree between the two iris codes. The Hamming distance is the matching metric employed by Daugman, and calculation of the Hamming distance is taken only with bits that are generated from the actual iris region. V.EXPERIMENT RESULT We tested our project in 100 pictures from CASIA database and obtain an average of correct recognition of 83 %. Some of the problems which produces noises are bad lighting, occlusion by eyelids, inappropriate eye positioning. Solution to this problem is taken as future enhancement. Figure 5. Image with parameters VI.CONCLUSION The physiological characteristics are relatively unique to an individual. An approach to reliable visual recognition of persons is achieved by iris patterns. We have successfully developed a new Iris recognition system capable of comparing two digital eye-images. This identification system is quite simple requiring few components and is effective enough to be integrated within security systems that require an identity check. The errors that occurred can be easily overcome by the use of stable equipment. Judging by the clear distinctiveness of the iris patterns we can expect iris recognition systems to become the leading technology in identity verification. VII.APPENDIX The following is the main program used to retrieve the data. function [template, mask] = mainprogram(eyeimage_filename) % diagnostic images global DIAGPATH DIAGPATH = 'diagnostics'; %normalisation parameters. radial_res = 20; angular_res = 240; %feature encoding parameters nscales=1; minwavelength=18; mult=1; % not applicable if using nscales = 1 sigmaonf=0.5; eyeimage = imread(eyeimage_filename); savefile = [eyeimage_filename,'- houghpara.mat']; [stat,mess]=fileattrib(savefile); if stat == 1 load(savefile); else [circleiris circlepupil imagewithnoise] = segmentiris(eyeimage); save(savefile,'circleiris','circlepupil', 'imagewithnoise'); end % write noise image imagewithnoise2 =int8(imagewithnoise); imagewithcircles = uint8(eyeimage); %get pixel coords for circle around iris [x,y] = circlecoords([circleiris(2), circleiris(1)], circleiris(3), size(eyeimage)); ind2 = sub2ind(size(eyeimage),double(y), double(x)); %get pixel coords for circle around pupil [xp,yp] = circlecoords([circlepupil(2), circlepupil(1)],circlepupil(3), size(eyeimage)); ind1 = sub2ind(size(eyeimage),double(yp)

5 ,double(xp)); % Write noise regions imagewithnoise2(ind2) = 255; imagewithnoise2(ind1) = 255; % Write circles overlayed imagewithcircles(ind2) = 255; imagewithcircles(ind1) = 255; w = cd; cd(diagpath); imwrite(imagewithnoise2, [eyeimage_filename, '-noise.jpg'],'jpg'); imwrite(imagewithcircles, [eyeimage_filename, '-segmented.jpg'],'jpg'); cd(w); % perform normalisation [polar_array noise_array] = normaliseiris(imagewithnoise, circleiris(2),... circleiris(1), circleiris(3), circlepupil(2), circlepupil(1), circlepupil(3),eyeimage_filename, radial_res, angular_res); % Write Normalised Pattern, And Noise Pattern w = cd; cd(diagpath); imwrite(polar_array,[eyeimage_filename,'- polar.jpg'],'jpg'); imwrite(noise_array,[eyeimage_filename,'- polarnoise.jpg'],'jpg'); cd(w); % perform feature encoding [template mask] = encode(polar_array, noise_array, nscales, minwavelength, mult, sigmaonf); REFERENCE 1. A. Goshtasby, Piecewise linear mapping functions for image registration, Pattern Recognition, 19(6): , L. G. Brown, A survey of image registration techniques, ACM Computing Surveys, 24(4): , R.C. Gonzales R.E. Woods. Digital Image Processing. Addison-Wesley Publishing Company, B.S. Reddy B.N. Chatterji. An fft-based technique for translation, rotation, and scaleinvariant image registration, IEEE Transactions on Image Processing, 5(8), Ale Muroò, JaroslavPospíil, The human iris structure and Its usages,.acta Univ. Palacki.Olomuc.,Fac. Rer. at, (2000). 6. C. H. Daouk, L. A. El-Esber, F. D.Kammoun, M. A. Al Alaoui, Iris Recognition, IEEE ISSPIT Libor Masek, Recognition of Human Iris Patterns for Biometric Identification, Ahmad Poursaberi and Babak N. Araabi, A Novel Iris Recognition System Using Morphological Edge Detector and Wavelet Phase Features, GVIP(05), No.V6, 2005, pp Junzhou Huang, Tieniu Tan, Li Ma, Yunhong Wang, Phase Correlation Based Iris Image Registration Model, Journal of Computer Science and Technology, Vol. 20, No. 2, pp , March Miyazawa.K, K.Aoki, T.Kobayashi, K.Nakajima, An Implementation-Oriented Iris Recognition Algorithm Using Phase- Based Image Matching. Sendai Intelligent Signal Processing and Communications, ISPACS ' John Daugman, Dr. Mohamed A. Hebaishy, Optimized Daugman s Algorithm for Iris Localization,September Hongshi Yan, JianGuo Liu, Robust Phase Correlation Based Feature MatchinigFor Image Co-Registration And Dem Generation, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7,Beijing Rajesh Bodade,Dr Sanjay Talbar, Dynamic Iris Localisation: A Novel Approach suitable for Fake Iris Detection, IJCISIM, 3 Dec GaganpreetKaur, AkshayGirdhar, ManvjeetKaur, Enhanced Iris Recognition System an Integrated Approach to Person Identification, IJCA lyasuyanik, Xiaojing Yuan, iris recognition system using new segmentation method, IEEE Transaction, Jan GeorgiosTzimiropoulos,VasileiosArgyriou, StefanosZafeiriou and Tania Stathaki, Robust FFT-Based Scale-Invariant Image Registration with Image Gradients,Supplementary Material April 21, Dr.H.B.Kekre, Sudeep D. Thepade, Juhi Jain, NamanAgrawa, IRIS Recognition using Texture Features Extracted from Haarlet Pyramid, International Journal of Computer Applications ( ), Volume 11 No.12, December 2010.

A Novel Identification System Using Fusion of Score of Iris as a Biometrics

A Novel Identification System Using Fusion of Score of Iris as a Biometrics Raj Kumar Singh 1, Braj Bihari Soni 2 1 M. Tech Scholar, NIIST, RGTU, raj_orai@rediffmail.com, Bhopal (M.P.) India; 2 Assistant