A study of iris recognition methods

A STUDY OF PRE-PROCESSING METHODS FOR IRIS RECOGNITION BENOIT MORTGAT NATIONAL UNIVERSITY OF SINGAPORE 2009 A STUDY OF PRE-PROCESSING METHODS FOR IRIS RECOGNITION BENOIT MORTGAT (joint degree Télécom SudParis) A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ENGINEERING DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2009 Abstract In the context of an increasing demand of biometric solutions and growing market for security and authentication applications, iris recognition has potential for a wider use. The iris is particularly good for biometric purposes due to the high randomness of its texture, thus making each eye highly unique. Iris offers other various advantages such as universality and feeble intrusiveness. In our iris recognition process, the iridian region is first isolated using mathematical tools to find circles in images. The iris image is then passed through a set of Gabor filters, that offer a good localisation both in frequency and in space. Depending on the outputs of these filters, each pixel in the iris image is assigned to a class by a clustering program with a required number of classes. Comparison between two irises can be performed by measuring similarity between class repartition of the pixels. This algorithm gives out promising results that allow for future enhancements. * ** Keywords — Iris recognition Iris segmentation Clustering algorithm Automatic classification Gabor filter Biometrics CHAPTER 0. ACKNOWLEDGEMENTS Acknowledgements I would like to thank many in this thesis. My thoughts are first for the different people who supervised my work, namely Assoc. Prof. Ashraf, Kassim and Prof. Venkatesh, Yedatore Venkatakris. For their advice, support all along the duration of my research work. They provided significant help and gave me directions for my research. I owe them warmest thanks. The research work that is presented in this thesis has been made possible by the use of the UBIRIS database, from the University of Beira Interior, Portugal. I appreciated this precious help and express my most sincere gratitude to its creators. This iris database provided excellent data for this work. The present thesis and its data, graphics, etc. have been produced by numerous computer software, and I would like to particularly thank the creators of the CImg software library. In addition, creators of the TikZ-PGF library for LATEX did a work that particularly helped me. Eventually, I address my thanks to all the people who encouraged me throughout my work, those whom I am indebted to, my family and friends. ii CHAPTER 0. ACKNOWLEDGEMENTS Contents Acknowledgements ii Introduction 1.1 About biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Iris as a biometric feature . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Anatomy of the iris . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Biometric features . . . . . . . . . . . . . . . . . . . . . . . Overview of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Previous works 10 2.1 On textures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 On iris recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Recognition 15 3.1 Iris contour extraction . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.1.1 Overview of the problem . . . . . . . . . . . . . . . . . . . 15 3.1.2 Problems to overcome . . . . . . . . . . . . . . . . . . . . . 16 3.1.3 Our algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Iridian meshwork filtering . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.1 Gabor filters . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.2 Gabor filter sets . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2 iii CHAPTER 0. ACKNOWLEDGEMENTS 3.3 3.4 29 3.3.1 The algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3.2 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Measuring similarity . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Results and discussions 36 4.1 About The database UBIRIS . . . . . . . . . . . . . . . . . . . . . . 36 4.1.1 Advantages of UBIRIS . . . . . . . . . . . . . . . . . . . . . 38 4.1.2 Drawbacks of UBIRIS . . . . . . . . . . . . . . . . . . . . . . 38 Segmentation results . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2.1 Hough transform . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2.2 Finding the location of the sclera (outer circle) . . . . . . . 41 4.2.3 Finding the location of the pupil (inner circle) . . . . . . . 43 4.2.4 Global segmentation results, and discussion . . . . . . . . 45 4.3 Filtering results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.4 Clustering results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.5 Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.6 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion 57 Bibliography 60 Appendices 63 A Source code 63 A.1 Segmentation source code . . . . . . . . . . . . . . . . . . . . . . . 63 A.1.1 ChangeGeometry.H . . . . . . . . . . . . . . . . . . . . . . 64 A.1.2 ChangeGeometry.C . . . . . . . . . . . . . . . . . . . . . . . 65 A.1.3 Common.H . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 A.1.4 Drawing.H . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 iv CHAPTER 0. ACKNOWLEDGEMENTS A.1.5 Drawing.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 A.1.6 FindCircles.H . . . . . . . . . . . . . . . . . . . . . . . . . . 69 A.1.7 FindCircles.C . . . . . . . . . . . . . . . . . . . . . . . . . . 71 A.1.8 Hough.H . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 A.1.9 Hough.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 A.1.10 Segment.H . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 A.1.11 Segment.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 A.1.12 Makefile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 A.2 Filtering source code . . . . . . . . . . . . . . . . . . . . . . . . . . 80 A.2.1 filters.conf . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 A.2.2 Gabor.H . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 A.2.3 Gabor.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 A.2.4 Makefile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 A.3 Clustering source code . . . . . . . . . . . . . . . . . . . . . . . . . 89 A.3.1 Makefile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 A.3.2 ClassesC.H . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 A.3.3 ClassesC.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 A.3.4 Clustering.H . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 A.3.5 Clustering.C . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 A.4 Matching source code . . . . . . . . . . . . . . . . . . . . . . . . . . 102 A.4.1 Makefile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 A.4.2 Matching.C . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 B Example of use 106 B.1 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 B.2 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 B.3 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 B.4 Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 C The PGM file format 112 v CHAPTER 0. ACKNOWLEDGEMENTS Index 113 vi LIST OF TABLES LIST OF TABLES List of Tables 4.1 Success rates for finding the contour of the sclera; changing the angular aperture for computation. . . . . . . . . . . . . . . . . . . 4.2 41 Success rates for finding the contour of the sclera; changing the parameter po in the expression of β(ρ) for computation. . . . . . . 42 4.3 Influence of µ0 on success rate . . . . . . . . . . . . . . . . . . . . . 44 4.4 Influence of ξ on segmentation success rate when µ0 has been optimised . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.5 Different filter sets . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.6 Speed of the similarity measure algorithm . . . . . . . . . . . . . . 56 vii LIST OF FIGURES LIST OF FIGURES List of Figures 1.1 Several fingerprint readers . . . . . . . . . . . . . . . . . . . . . . . 1.2 Steps in biometrics applications . . . . . . . . . . . . . . . . . . . . 1.3 Several figures, from [2, section X-1c] . . . . . . . . . . . . . . . . . 3.1 Segmentation of the eye. . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Zoom on the border of an iris . . . . . . . . . . . . . . . . . . . . . 18 3.3 Y (x, y, ρ) is roughly the integral of γ(∆I)dθ where ∆I is the intensity difference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 20 Non-linear filter on luminance to diminish the effect of light reflection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.5 Pixel interpolation for storage of segmentation results . . . . . . . 26 3.6 A Gabor filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.7 2D Gabor filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.8 Sample chequerboard . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.9 Running the clustering algorithm on a random set of 2D vectors . 33 4.1 Sample images from the UBIRIS database . . . . . . . . . . . . . . 37 4.2 A successful segmentation example . . . . . . . . . . . . . . . . . 39 4.3 A Hough transform can detect circles efficiently . . . . . . . . . . 40 4.4 The parameter po controls the desired radius . . . . . . . . . . . . 43 4.5 Some typical segmentation outputs. . . . . . . . . . . . . . . . . . 46 viii A.3. CLUSTERING SOURCE CODE 75 80 85 90 95 100 105 APPENDIX A. SOURCE CODE bool allocateMemory ( void ) { bool rv= t r u e ; dblArrray = new double∗ [ numVectors ] ; toBeMovedTo = new u i n t [ numVectors ] ; myVects = new Vect [ numVectors ] ; classes = new u i n t [ numVectors ] ; variances = new Vect [ ∗ numClasses ] ; myClusters = new Clu [ ∗ numClasses ] ; initVects = new Vect [ numClasses ] ; i f ( dblArrray ! = NULL) LOOP ( i , , numVectors ) { dblArrray [ i ] = new double [ dim ] ; i f ( dblArrray [ i ] == NULL) rv= f a l s e ; } i f ( dblArrray == NULL || c l a s s e s == NULL || toBeMovedTo == NULL || myVects == NULL || myClusters == NULL || v a r i a n c e s == NULL || i n i t V e c t s == NULL || rv == f a l s e ) return false ; return true ; } void wiper ( void ) { inFile . close ( ) ; i f ( dblArrray ! = NULL) { LOOP ( i , , numVectors ) i f ( dblArrray [ i ] ! = NULL) d e l e t e [ ] dblArrray [ i ] ; d e l e t e [ ] dblArrray ; dblArrray = NULL; } if ( classes ! = NULL) { d e l e t e [ ] c l a s s e s ; c l a s s e s = NULL; } i f ( toBeMovedTo ! = NULL) { d e l e t e [ ] toBeMovedTo ; toBeMovedTo = NULL; } i f ( myVects ! = NULL) { d e l e t e [ ] myVects ; myVects = NULL; } i f ( myClusters ! = NULL) { d e l e t e [ ] myClusters ; myClusters = NULL; } i f ( variances ! = NULL) { d e l e t e [ ] v a r i a n c e s ; v a r i a n c e s = NULL; } if ( initVects ! = NULL) { d e l e t e [ ] i n i t V e c t s ; i n i t V e c t s = NULL; } return ; } 110 115 120 125 130 135 140 bool parseCommandLine ( i n t argc , char ∗∗ argv ) { i f ( argc != 6) { s t d : : c e r r [...]... content-based image search In his Ph.D thesis1 , Hafiane investigates methods of texture analysis, with the help of several mathematical tools (statistics of the image, frequency analysis, use of Peano curves and local trajectories, or description by characteristic matrix of the patterns) and evaluates performance using a texture database and classifying his segmentation results with the knearest-neighbours... Nevertheless, many aspects can be questioned, that are tentatively listed 1 In the formula, the angular values are arbitrary and can vary 16 3.1 IRIS CONTOUR EXTRACTION CHAPTER 3 RECOGNITION here Of the shape of the iris The circular shape of the iris can be questioned Actually, it is more generally assumed that the shape is rather elliptic The reasons for this include gravity, and camera positioning Of the... recognition can be performed by measuring the Hamming distance2 between a measured iris code and a database iris code A study of the repartition of the Hamming distances measured on a significant number of iris codes led Daugman to approximate the number of binary degrees of freedom in an iris code as approximately 173, which means that the likelihood for two iris codes from different people to match in... Registering a person or class in a database of features is called training, whereas the identification operation consists of using training data to determine whether it matches a measure The different steps shown on Figure 1.2 are detailed here: Capture: the step in which data is obtained from an individual Capture can be realised with the help of an optical system like a camera in the case of fingerprint, iris. .. the iris is tweaked with a thresholding of gradient magnitude and help of the Hough transform Moreover a registration technique is used to compensate for scaling and rotation The feature extraction is not done with the help of Gabor filters, but with Laplacian of Gaussian filters Similarity to data base is measured with a correlation definition Both Wildes and Daugman systems have been awarded with US patents... natural and artificial images In a next part, Hafiane applies these results to Content-Based Image Retrieval (CBIR) The main issues with CBIR are explained (including what it requires in terms of spatial layout of the image elements) Different search techniques are presented with performance evaluation The complete automated chain of algorithms developed in this thesis is dedicated to analysis of satellite... classification of images like iris recognition often involves different ways of extracting textural information from the inputs Texture characterisation is a field that has been flourishing for two or three decades and that is made easier by new mathematical tools like wavelets There is abundance of literature on texture classification Hafiane, in [7, in French], has done much work on texture analysis and content-based... interpreted as a type of normalisation Outer circle Basically, the first circle that is “looked” for is the outer circle, that separates the sclera and the iris There are several reasons to choose this circle first, that are tentatively listed here: • The border between the sclera and the iris has a better contrast The irises are heavily pigmented, and even in cases of albinism, an iris area will be seen as red... concentricity of the two boundaries Much work on recognition state that the centre of the pupil is not always exactly at the same location as the centre of the iris[ 11, 4] Therefore, the shape of the iris is not mathematically an annulus, but could be rather defined as a “doughnut” Of the horizontality of the head A biometric system must ensure that the information that is captured is stable If a person... deliver access to a part of a building Capture Capture Segmentation Segmentation Feature Extraction Feature Extraction Comparison Database Classification (a) Training Database (b) Identifying Figure 1.2: Steps in biometrics applications As any pattern recognition system, the biometric based recognition system involves several steps, as shown in Figure 1.2 and these the steps could vary depending on the goal . work, namely Assoc. Prof. Ashraf, Kassim and Prof. Venkatesh, Yedatore Venkatakris. For their advice, support all along the duration of my research work. They provided significant help and gave me directions. page shows several fingerprint readers; the last one is sometimes used to deliver access to a part of a building. Capture Segmentation Feature Extraction Database (a) Training Capture Segmentation Feature. individual. Capture can be realised with the help of an optical system like a camera in the case of fingerprint, iris or gait recognition, or a microphone in the case of voice, for example. Segmentation: