Chun, Chun Nam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 73-75). / Abstracts in English and Chinese. / ABSTRACT --- p.i / 摘要 --- p.ii / ACKNOWLEDGEMENTS --- p.iii / TABLE OF CONTENTS --- p.iv / LIST OF FIGURES --- p.v / LIST OF TABLES --- p.vii / Chapter 1 --- INTRODUCTION --- p.01 / Chapter 1.1 --- Iris Texture as A Biometric Password --- p.01 / Chapter 1.1.1 --- Advantages --- p.02 / Chapter 1.1.2 --- Previous Work --- p.04 / Chapter 1.1.3 --- Operation Procedures --- p.06 / Chapter 1.1.3.1 --- Image Acquisition --- p.07 / Chapter 1.1.3.2 --- Iris Localization --- p.07 / Chapter 1.1.3.3 --- Image Encoding and Database Matching --- p.08 / Chapter 1.2 --- Motivation and Research Objective --- p.09 / Chapter 1.3 --- Thesis Outline --- p.11 / Chapter 2 --- IMAGE ACQUISITION --- p.13 / Chapter 2.1 --- Difficulties on Image Acquisition --- p.13 / Chapter 2.2 --- Our Iris Image Acquisition Setting --- p.13 / Chapter 3 --- PRE-PROCESSING --- p.15 / Chapter 3.1 --- Isolating the Region of Interest --- p.15 / Chapter 3.1.1 --- Conscious and Unconscious Recognition --- p.15 / Chapter 3.1.2 --- Iris Boundary Detection --- p.16 / Chapter 3.2 --- Iris-ring Unfolding and Normalization --- p.19 / Chapter 3.2.1 --- Eccentric-polar Coordinate System --- p.20 / Chapter 3.2.2 --- Iris-ring Unfolding --- p.22 / Chapter 3.2.3 --- Normalization --- p.22 / Chapter 3.3 --- Data Binarization --- p.24 / Chapter 4 --- RADON TRANSFORM BASED ENCODING AND MATCHING --- p.27 / Chapter 4.1 --- Radon Transform based Encoding --- p.28 / Chapter 4.2 --- Iris Code Matching --- p.32 / Chapter 4.2.1 --- Regional Correlation --- p.32 / Chapter 5 --- PALM-TOP IMPLEMENTATION ON COMPUTING PLATFORM --- p.36 / Chapter 5.1 --- Image Acquisition --- p.37 / Chapter 5.1.1 --- Desktop Version --- p.37 / Chapter 5.1.2 --- Palm-top Version --- p.37 / Chapter 5.2 --- Iris Localization --- p.39 / Chapter 5.2.1 --- Desktop Version --- p.39 / Chapter 5.2.2 --- Palm-top Version --- p.39 / Chapter 5.3 --- Image Encoding --- p.41 / Chapter 5.3.1 --- Desktop Version --- p.41 / Chapter 5.3.2 --- Palm-top Version --- p.41 / Chapter 5.4 --- Palm-Top Computer Application --- p.42 / Chapter 5.4.1 --- Palm-top Computer Setting --- p.42 / Chapter 5.4.2 --- Software Selection --- p.42 / Chapter 5.4.3 --- Technical Problems --- p.43 / Chapter 5.4.3.1 --- Problem 1: Memory Limitation --- p.43 / Chapter 5.4.3.2 --- Problem 2: Image Format --- p.44 / Chapter 5.4.3.3 --- Problem 3: Origin of Image --- p.44 / Chapter 5.5 --- Our Iris Recognition Platform --- p.44 / Chapter 6 --- EXPERIMENTAL RESULTS --- p.47 / Chapter 6.1 --- The Test Data --- p.47 / Chapter 6.2 --- Experiment One: Eccentric Polar Coordinates System Recognition Performance --- p.48 / Chapter 6.2.1 --- Performance Measure of Recognition --- p.48 / Chapter 6.2.2 --- Experimental Result --- p.49 / Chapter 6.3 --- Experiment Two: Radon Transform-based Recognition System Performance --- p.53 / Chapter 6.3.1 --- Intra-group Similarity vs. Inter-group Similarity --- p.54 / Chapter 6.3.2 --- Performance Comparison with an Existing System --- p.57 / Chapter 6.4 --- Experiment Three: The Resolution of Image in the Eccentric-polar Coordinates System --- p.58 / Chapter 7 --- CONCLUSION AND FUTURE WORK --- p.62 / Chapter 7.1 --- Conclusion --- p.62 / Chapter 7.2 --- Future Work --- p.63 / APPENDIX A --- p.66 / APPENDIX B --- p.67 / BIBLIOGRAPHY --- p.73

No description available.

Call number: LD2668 .T4 1985 C348 / Master of Science

No description available.

Biometrics is the science and technology of measuring and analyzing biological data for authentication purposes. Its progress has brought in a large number of civilian and government applications. The candidate modalities used in biometrics include retinas, fingerprints, signatures, audio, faces, etc. There are two types of biometric system: single modal systems and multiple modal systems. Single modal systems perform person recognition based on a single biometric modality and are affected by problems like noisy sensor data, intra-class variations, distinctiveness and non-universality. Applying multiple modal systems that consolidate evidence from multiple biometric modalities can alleviate those problems of single modal ones. Integration of evidence obtained from multiple cues, also known as fusion, is a critical part in multiple modal systems, and it may be consolidated at several levels like feature fusion level, matching score fusion level and decision fusion level. Among biometric modalities, both audio and face modalities are easy to use and generally acceptable by users. Furthermore, the increasing availability and the low cost of audio and visual instruments make it feasible to apply such Audio-Visual (AV) systems for security applications. Therefore, this dissertation proposes an algorithm of face recognition. In addition, it has developed some novel algorithms of fusion in different levels for multiple modal biometrics, which have been tested by a virtual database and proved to be more reliable and robust than systems that rely on a single modality. / by Lin Huang. / Thesis (Ph.D.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

Biometric systems such as face, palm and fingerprint recognition are very computationally expensive. The ever growing biometric database sizes have posed a need for faster search algorithms. High resolution images are expensive to process and slow down less powerful extraction algorithms. There is an apparent need to improve both the signal processing and the searching algorithms. Researchers have continually searched for new ways of improving the recognition algorithms in order to keep up with the high pace of the scientific and information security world. Most such developments, however, are architecture- or hardware-specific and do not port well to other platforms. This research proposes a cheaper and portable alternative. With the use of the Single Program Multiple Data programming architecture, a distributed fingerprint recognition algorithm is developed and executed on a powerful cluster. The first part in the parallelization of the algorithm is distributing the image enhancement algorithm which comprises of a series of computationally intensive image processing operations. Different processing elements work concurrently on different parts of the same image in order to speed up the processing. The second part of parallelization speeds up searching/ matching through a parallel search. A database is partitioned as evenly as possible amongst the available processing nodes which work independently to search their respective partitions. Each processor returns a match with the highest similarity score and the template with the highest score among those returned is returned as match given that the score is above a certain threshold. The system performance with respect to response time is then formalized in a form of a performance model which can be used to predict the performance of a distributed system given network parameters and number of processing nodes. The proposed algorithm introduces a novel approach to memory distribution of block-wise image processing operations and discusses three different ways to process pixels along the partitioning axes of the distributed images. The distribution and parallelization of the recognition algorithm gains up to as much as 12.5 times performance in matching and 10.2 times in enhancement. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2012.