A Generic Computer Platform for Efficient Iris Recognition
Total Page：16
File Type：pdf, Size：1020Kb
n Ponder, Christopher John (2015) A generic computer platform for efficient iris recognition. EngD thesis. http://theses.gla.ac.uk/6780/ Copyright and moral rights for this thesis are retained by the author A copy can be downloaded for personal non-commercial research or study, without prior permission or charge This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the Author The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the Author When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given Glasgow Theses Service http://theses.gla.ac.uk/ [email protected] A Generic Computer Platform For Efficient Iris Recognition Christopher John Ponder BEng (Hons) A thesis submitted to the Universities of Edinburgh, Glasgow, Heriot-Watt, and Strathclyde For the Degree of Doctor of Engineering in System Level Integration © Christopher John Ponder 2014 To Rachel, Alex and Ben Abstract This document presents the work carried out for the purposes of completing the Engineering Doctorate (EngD) program at the Institute for System Level Integration (iSLI), which was a partnership between the universities of Edinburgh, Glasgow, Heriot- Watt and Strathclyde. The EngD is normally undertaken with an industrial sponsor, but due to a set of unforeseen circumstances this was not the case for this work. However, the work was still undertaken to the same standards as would be expected by an industrial sponsor. An individual’s biometrics include fingerprints, palm-prints, retinal, iris and speech patterns. Even the way people move and sign their name has been shown to be uniquely associated with that individual. This work focuses on the recognition of an individual’s iris patterns. The results reported in the literature are often presented in such a manner that direct comparison between methods is difficult. There is also minimal code resource and no tool available to help simplify the process of developing iris recognition algorithms, so individual developers are required to write the necessary software almost every time. Finally, segmentation performance is currently only measurable using manual evaluation, which is time consuming and prone to human error. This thesis presents a completely novel generic platform for the purposes of developing, testing and evaluating iris recognition algorithms which is designed to simplify the process of developing and testing iris recognition algorithms. Existing open-source algorithms are integrated into the generic platform and are evaluated using the results it produces. Three iris recognition segmentation algorithms and one normalisation algorithm are proposed. Three of the algorithms increased true match recognition performance by between two and 45 percentage points when compared to the available open-source algorithms and methods found in the literature. A matching algorithm was developed that significantly speeds up the process of analysing the results of encoding. Lastly, this work also proposes a method of automatically evaluating the performance of segmentation algorithms, so minimising the need for manual evaluation. Declaration of Originality I, Christopher John Ponder, declare that this thesis is my own work and has not been submitted in any form for another degree or diploma at any university or other institute of tertiary education. Information derived from the published and unpublished work of others has been acknowledged in the text and a list of references is given in the bibliography. The material contained in this thesis is my own original work produced under the supervision of Khaled Benkrid and Martin Reekie. Dedication and Acknowledgements This thesis is dedicated to my family, Rachel, Alex & Ben, whose love, encouragement and understanding made this possible. I would like to thank Khaled Benkrid, my academic supervisor for the first three years of my research, for believing in me and for his support and encouragement. I would like to express my sincere gratitude and deepest appreciation to Martin Reekie who has been there for me though-out my journey and has been my academic supervisor for the last two years. Their support and encouragement over the course of my studies have been invaluable. I would like to thank Theresa, for her understanding, kind words and sympathetic ear, without which this would have been a far more difficult journey. I would also like to thank the people at Tritech International Limited for their patience, understanding and timely assistance during the writing of this thesis. Finally, I would like to thank the all staff at the iSLI and Glasgow University for their help and support throughout my studies. Page 6 of 380 Portions of the research in this thesis use the CASIA-Iris V1, V2, V3 and V4 image datasets collected by the Centre for Biometrics and Security Research at the Chinese Academy of Sciences Institute of Automation, http://biometrics.idealtest.org/ , and http://www.cbsr.ia.ac.cn/IrisDataset.htm Portions of the research in this thesis use the MMU1 and MMU2 image datasets, http://pesona.mmu.edu.my/~ccteo/ Portions of the research in this thesis use the UBIRIS V1 and V2 image datasets collected by Hugo Pedro Proença and Luís A. Alexandre of Department of Computer Science, University of Beira Interior, http://iris.di.ubi.pt/ubiris1.html and http://iris.di.ubi.pt/ubiris2.html Portions of the research in this thesis were tested using version 1.0 of the IITD Iris Database http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm Table of Contents Abstract ................................................................................................................................. 3 Dedication and Acknowledgements .................................................................................... 5 List of Figures ..................................................................................................................... 12 List of Tables ...................................................................................................................... 19 List of Image Datasets ........................................................................................................ 23 List of Symbols ................................................................................................................... 24 Glossary ............................................................................................................................... 27 1 Introduction ................................................................................................................. 34 1.1 Motivation ............................................................................................................... 35 1.2 Thesis Outline ......................................................................................................... 39 1.3 Thesis Outcomes ..................................................................................................... 43 2 Literature Review ........................................................................................................ 45 2.1 Image Capture ......................................................................................................... 48 2.2 Pre-Processing ........................................................................................................ 54 2.3 Segmentation .......................................................................................................... 56 2.4 Normalisation.......................................................................................................... 61 2.5 Encoding ................................................................................................................. 64 2.6 Matching ................................................................................................................. 68 2.7 Generic Platform ..................................................................................................... 75 2.8 Conclusions ............................................................................................................. 77 3 Designing the Generic Platform ................................................................................. 78 3.1 Development Environment and Coding Language ................................................. 81 3.1.1 Background and Thinning the Field .................................................................. 81 3.1.2 C/C++ ................................................................................................................ 82 3.1.3 Java .................................................................................................................... 83 3.1.4 MATLAB .......................................................................................................... 84 3.1.5 FreeMAT ........................................................................................................... 85 3.1.6 Octave................................................................................................................ 86 3.1.7 SciLab................................................................................................................ 86 Page 8 of 380 3.1.8 Conclusion ......................................................................................................... 87 3.2 Measurable Algorithm Performance via