M.Com. (Informatics) / A certain degree of vulnerability exists in traditional knowledge-based identification and authentication access control, as a result of password interception and social engineering techniques. This vulnerability has warranted the exploration of additional identification and authentication approaches such as physical token-based systems and biometrics. Speaker recognition is one such biometric approach that is currently not widely used due to its inherent technological challenges, as well as a scarcity of comprehensive literature and complete open-source projects. This makes it challenging for anyone who wishes to study, develop and improve upon speaker recognition for identification and authentication. In this dissertation, we condense some of the available speaker recognition literature in a manner that would provide a comprehensive overall picture of speaker identification and authentication to a wider range of interested audiences. A speaker recognition solution in the form of an open, user-friendly software prototype environment is presented, called SRIA (Speaker Recognition Identification Authentication). In SRIA, real users may enrol and perform speaker identification and authentication tasks. SRIA is intended as platform for speaker recognition understanding and further research and development.

The need for a more secure cyber future is apparent in the information age that we live in. Information is fast becoming, and already is, one of the biggest assets in all domains of life. Access to information and specifically personal information must be regulated and secured in a trusted way. The use of passwords and tokens (example: bank card) that’s currently the most popular and well known mechanism for electronic identification can only identify the password or token but NOT the physical user using the password or token for identification. Biometrics addresses the above issue by being part of the physical user. For example: your fingerprint, retina or iris. Current biometric technologies provide an enabling medium to help with more accurate identification and verification. Thereby protecting and securing electronic information…BUT: One of the biggest problem areas surrounding biometrics is the fact that most biometric tokens (fingerprints, hand geometry and the human eye) can be used in some cases to identify the owner of the biometric token even after death as if the owner was still alive. The problem becomes apparent in the case of a person that passed away and the possibility of using the biometric tokens of the deceased to obtain access to his/her bank account. Therefore the importance of effective liveness testing is highlighted. Current liveness testing technologies can not be trusted in a way that would be necessary to provide the trust needed in the example of access to a personal bank account at an ATM (automatic teller machine). This dissertation reports on the initial stages of a research project that addresses the above problem by proposing the use of biometric tokens that doesn’t exist if the owner is not alive, thus the dissertation coins the new term – Inherent Liveness Biometrics. The way the human heart beats as a biometric token to identify or verify a person, might solve the issue of liveness testing, because “The way the human heart beats” might prove to be a natural biometric token that is only valid for a living person, thus an inherent liveness biometric. / Prof. S.H. Von Solms

D.Com. (Informatics) / Please refer to full text to view abstract

by Zhao Feng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 64-68). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.vi / Table of Contents --- p.vii / List of Figures --- p.ix / List of Tables --- p.x / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Automatic Personal Identification --- p.1 / Chapter 1.2 --- Biometrics --- p.2 / Chapter 1.2.1 --- Objectives --- p.2 / Chapter 1.2.2 --- Operational Mode --- p.3 / Chapter 1.2.3 --- Requirements --- p.3 / Chapter 1.2.4 --- Performance Evaluation --- p.4 / Chapter 1.2.5 --- Biometric Technologies --- p.4 / Chapter 1.3 --- Fingerprint --- p.6 / Chapter 1.3.1 --- Applications --- p.6 / Chapter 1.3.2 --- Advantages of Fingerprint Identification --- p.7 / Chapter 1.3.3 --- Permanence and Uniqueness --- p.8 / Chapter 1.4 --- Thesis Overview --- p.8 / Chapter 1.5 --- Summary --- p.9 / Chapter Chapter 2 --- Fingerprint Identification --- p.10 / Chapter 2.1 --- History of Fingerprints --- p.10 / Chapter 2.2 --- AFIS Architecture --- p.12 / Chapter 2.3 --- Fingerprint Acquisition --- p.15 / Chapter 2.4 --- Fingerprint Representation --- p.16 / Chapter 2.5 --- Fingerprint Classification --- p.18 / Chapter 2.6 --- Fingerprint Matching --- p.20 / Chapter 2.7 --- Challenges --- p.21 / Chapter 2.8 --- Combination Schemes --- p.22 / Chapter 2.9 --- Summary --- p.23 / Chapter Chapter 3 --- Live-Scan Fingerprint Database --- p.24 / Chapter 3.1 --- Live-Scan Fingerprint Sensors --- p.24 / Chapter 3.2 --- Database Features --- p.24 / Chapter 3.3 --- Filename Description --- p.28 / Chapter Chapter 4 --- Preprocessing for Skeleton-Based Minutiae Extraction --- p.30 / Chapter 4.1 --- Review of Minutiae-based Methods --- p.31 / Chapter 4.2 --- Skeleton-based Minutiae Extraction --- p.32 / Chapter 4.2.1 --- Preprocessing --- p.33 / Chapter 4.2.2 --- Validation of Bug Pixels and Minutiae Extraction --- p.40 / Chapter 4.3 --- Experimental Results --- p.42 / Chapter 4.4 --- Summary --- p.44 / Chapter Chapter 5 --- Post-Processing --- p.46 / Chapter 5.1 --- Review of Post-Processing Methods --- p.46 / Chapter 5.2 --- Post-Processing Algorithms --- p.47 / Chapter 5.2.1 --- H-Point --- p.47 / Chapter 5.2.2 --- Termination/Bifurcation Duality --- p.48 / Chapter 5.2.3 --- Post-Processing Procedure --- p.49 / Chapter 5.3 --- Experimental Results --- p.52 / Chapter 5.4 --- Summary --- p.54 / Chapter Chapter 6 --- Conclusions and Future Work --- p.58 / Chapter 6.1 --- Conclusions --- p.58 / Chapter 6.2 --- Problems and Future Works --- p.59 / Chapter 6.2.1 --- Problem 1 --- p.59 / Chapter 6.2.2 --- Problem 2 --- p.61 / Chapter 6.2.3 --- Problem 3 --- p.61 / Chapter 6.2.4 --- Future Works --- p.62 / Bibliography --- p.64

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

說話人識別(Speaker Recognition) 主要利用聲音來檢測說話人的身份，是一項重要且極具挑戰性的生物認證研究課題。通常來說，針對語音信號的文本內容差別，說話人識別可以分成文本相關和文本無關兩類。另外，說話人識別有兩類重要應用，第一類是說話人確認，主要是通過給定話者聲音信息對說話人聲稱之身份進行二元判定。另一類是說話人辨識，其主要是從待選說話人集中判斷未知身份信息的話者身份。 / 在先進的說話人識別系統中，每個說話人模型是通過給定的說話人數據進行特徵統計分佈估計由生成模型訓練得到。這類方法由於需要逐帧進行概率或似然度計算而得出最終判決，會耗費大量系統資源並降低實時性性能。採用子空間降維技術，我們不僅避免選取冗餘高維度數據，同時能夠有效删除於識別中無用之數據。為克服上述生成性模型的不足並獲得不同說話人間的區分邊界，本文提出了利用區分性子空間方法訓練模型並採用有效的距離測度作為最終的建模識別新算法。 / 在本篇論文中，我們將先介紹並分析各類產生性說話人識別方法，例如高斯混合模型及聯合因子分析。另外，為了降低特徵空間維度和運算時間，我們也對子空間分析技術做了調研。除此之外，我們提出了一種取名為Fishervoice 基於非參數分佈假定的新穎說話人識別框架。所提出的Fishervoice 框架的主要目的是為了降低噪聲干擾同時加重分類信息，而能夠加強在可區分性的子空間內對聲音特徵建模。採用上述Fishervoice 框架，說話人識別可以簡單地通過測試樣本映射到Fishervoice 子空間並計算其簡單歐氏距離而實現。為了更好得降低維度及提高識別率，我們還對Fishervocie 框架進行多樣化探索。另外，我們也在低維度的全變化空間(Total Variability) 對各類多種子空間分析模型進行調比較。基於XM2VTS 和NIST 公開數據庫的實驗驗證了本文提出的算法的有效性。 / Speaker Recognition (SR), which uses the voice to determine the speaker’s identity, is an important and challenging research topic for biometric authentication. Generally speaking, speaker recognition can be divided into text-dependent and text-independent methods according to the verbal content of the speech signal. There are two major applications of speaker recognition: the first is speaker verification, also referred to speaker authentication, which is used to validate the identity of a speaker according to the voice and it involves a binary decision. The second is speaker identification, which is used to determine an unknown speaker’s identity. / In a state-of-art speaker recognition system, the speaker training model is usually trained by generative methods, which estimate feature distribution of each speaker among the given data. These generative methods need a frame-based metric (e.g. probability, likelihoods) calculation for making final decision, which consumes much computer resources, slowing down the real-time responses. Meanwhile, lots of redundant data frames are blindly selected for training without efficient subspace dimension reduction. In order to overcome disadvantages of generative methods and obtain boundary information between individual speakers, we propose to apply the discriminative subspace technique for model training and employ simple but efficient distance metrics for decision score calculation. / In this thesis, we shall present an overview of both conventional and state-of-the-art generative speaker recognition methods (e.g. Gaussian Mixture Model and Joint Factor Analysis) and analyze their advantages and disadvantages. In addition, we have also made an investigation of the application of subspace analysis techniques to reduce feature dimensions and computation time. After that, a novel speaker recognition framework based on the nonparametric Fisher’s discriminant analysis which we name Fishervoice is proposed. The objective of the proposed Fishervoice algorithm is to model the intrinsic vocal characteristics in a discriminant subspace for de-emphasizing unwanted noise variations and emphasizing classification boundaries information. Using the proposed Fishervoice framework, speaker recognition can be easily realized by mapping a test utterance to the Fishervoice subspace and then calculating the score between the test utterance and its reference. Besides, we explore the proposed Fishervoice framework with several extensions for further dimensionality reduction and performance improvement. Furthermore, we investigate various subspace analysis techniques in a total variability-based low-dimensional space for fast computation. Extensive experiments on two large speaker recognition corpora (XM2VTS and NIST) demonstrate significant improvements of Fishervoice over standard, state-of-the-art approaches for both speaker identification and verification systems. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Jiang, Weiwu. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 127-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgements --- p.vi / Contents --- p.xiv / List of Figures --- p.xvii / List of Tables --- p.xxiii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview of Speaker Recognition Systems --- p.1 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.3 --- Outline of Thesis --- p.6 / Chapter 2 --- Background Study --- p.7 / Chapter 2.1 --- Generative Gaussian Mixture Model (GMM) --- p.7 / Chapter 2.1.1 --- Basic GMM --- p.7 / Chapter 2.1.2 --- The Gaussian Mixture Model-Universal Background Model (GMM-UBM) System --- p.9 / Chapter 2.2 --- Discriminative Subspace Analysis --- p.12 / Chapter 2.2.1 --- Principal Component Analysis --- p.12 / Chapter 2.2.2 --- Linear Discriminant Analysis --- p.16 / Chapter 2.2.3 --- Heteroscedastic Linear Discriminant Analysis --- p.17 / Chapter 2.2.4 --- Locality Preserving Projections --- p.18 / Chapter 2.3 --- Noise Compensation --- p.20 / Chapter 2.3.1 --- Eigenvoice --- p.20 / Chapter 2.3.2 --- Joint Factor Analysis --- p.24 / Chapter 2.3.3 --- Probabilistic Linear Discriminant Analysis --- p.26 / Chapter 2.3.4 --- Nuisance Attribute Projection --- p.30 / Chapter 2.3.5 --- Within-class Covariance Normalization --- p.32 / Chapter 2.4 --- Support Vector Machine --- p.33 / Chapter 2.5 --- Score Normalization --- p.35 / Chapter 2.6 --- Summary --- p.39 / Chapter 3 --- Corpora for Speaker Recognition Experiments --- p.41 / Chapter 3.1 --- Corpora for Speaker Identification Experiments --- p.41 / Chapter 3.1.1 --- XM2VTS Corpus --- p.41 / Chapter 3.1.2 --- NIST Corpora --- p.42 / Chapter 3.2 --- Corpora for Speaker Verification Experiments --- p.45 / Chapter 3.3 --- Summary --- p.47 / Chapter 4 --- Performance Measures for Speaker Recognition --- p.48 / Chapter 4.1 --- Performance Measures for Identification --- p.48 / Chapter 4.2 --- Performance Measures for Verification --- p.49 / Chapter 4.2.1 --- Equal Error Rate --- p.49 / Chapter 4.2.2 --- Detection Error Tradeoff Curves --- p.49 / Chapter 4.2.3 --- Detection Cost Function --- p.50 / Chapter 4.3 --- Summary --- p.51 / Chapter 5 --- The Discriminant Fishervoice Framework --- p.52 / Chapter 5.1 --- The Proposed Fishervoice Framework --- p.53 / Chapter 5.1.1 --- Feature Representation --- p.53 / Chapter 5.1.2 --- Nonparametric Fisher’s Discriminant Analysis --- p.55 / Chapter 5.2 --- Speaker Identification Experiments --- p.60 / Chapter 5.2.1 --- Experiments on the XM2VTS Corpus --- p.60 / Chapter 5.2.2 --- Experiments on the NIST Corpus --- p.62 / Chapter 5.3 --- Summary --- p.64 / Chapter 6 --- Extension of the Fishervoice Framework --- p.66 / Chapter 6.1 --- Two-level Fishervoice Framework --- p.66 / Chapter 6.1.1 --- Proposed Algorithm --- p.66 / Chapter 6.2 --- Performance Evaluation on the Two-level Fishervoice Framework --- p.70 / Chapter 6.2.1 --- Experimental Setup --- p.70 / Chapter 6.2.2 --- Performance Comparison of Different Types of Input Supervectors --- p.72 / Chapter 6.2.3 --- Performance Comparison of Different Numbers of Slices --- p.73 / Chapter 6.2.4 --- Performance Comparison of Different Dimensions of Fishervoice Projection Matrices --- p.75 / Chapter 6.2.5 --- Performance Comparison with Other Systems --- p.77 / Chapter 6.2.6 --- Fusion with Other Systems --- p.78 / Chapter 6.2.7 --- Extension of the Two-level Subspace Analysis Framework --- p.80 / Chapter 6.3 --- Random Subspace Sampling Framework --- p.81 / Chapter 6.3.1 --- Supervector Extraction --- p.82 / Chapter 6.3.2 --- Training Stage --- p.83 / Chapter 6.3.3 --- Testing Procedures --- p.84 / Chapter 6.3.4 --- Discussion --- p.84 / Chapter 6.4 --- Performance Evaluation of the Random Subspace Sampling Framework --- p.85 / Chapter 6.4.1 --- Experimental Setup --- p.85 / Chapter 6.4.2 --- Random Subspace Sampling Analysis --- p.87 / Chapter 6.4.3 --- Comparison with Other Systems --- p.90 / Chapter 6.4.4 --- Fusion with the Other Systems --- p.90 / Chapter 6.5 --- Summary --- p.92 / Chapter 7 --- Discriminative Modeling in Low-dimensional Space --- p.94 / Chapter 7.1 --- Discriminative Subspace Analysis in Low-dimensional Space --- p.95 / Chapter 7.1.1 --- Experimental Setup --- p.96 / Chapter 7.1.2 --- Performance Evaluation on Individual Subspace Analysis Techniques --- p.98 / Chapter 7.1.3 --- Performance Evaluation on Multi-type of Subspace Analysis Techniques --- p.105 / Chapter 7.2 --- Discriminative Subspace Analysis with Support Vector Machine --- p.115 / Chapter 7.2.1 --- Experimental Setup --- p.116 / Chapter 7.2.2 --- Performance Evaluation on LDA+WCCN+SVM --- p.117 / Chapter 7.2.3 --- Performance Evaluation on Fishervoice+SVM --- p.118 / Chapter 7.3 --- Summary --- p.118 / Chapter 8 --- Conclusions and Future Work --- p.120 / Chapter 8.1 --- Contributions --- p.120 / Chapter 8.2 --- Future Directions --- p.121 / Chapter A --- EM Training GMM --- p.123 / Bibliography --- p.127

Finally, the research of classifying and identifying individuals through their walking patterns is introduced. Alive biometrical features in dynamic human gait are adopted in the intelligent shoe system. Since gait data are dynamic, non-linear, stochastic, time-varying, noisy and multi-channel, we must select a modeling framework capable of dealing with these expected complexities in the data. Using the proposed machine learning methods, support vector machine (SVM) and hidden Markov models (HMMs), we build up probabilistic models that take the information of human walking patterns into account, and compare the overall similarity among human walking patterns of several wearers. / In this thesis, we will build intelligent shoes under the framework for capturing and analyzing dynamic human gait. Existing MEMS technology makes it possible to integrate all the sensors and circuits inside a small module. In designing our intelligent shoe system, we require the following key characteristics in our system: (1) It should be convenient to wear and socially acceptable. Thus, the sensors and electronic hardware installed should not substantially change the weight and weight balance of a typical shoe, lest it alters how an individual normally walks. (2) We want to analyze a user's motion in real-time through a wireless interface to a remote laptop or other computer; we will also incorporate on-shoe data logging hardware for off-line analysis. (3) Sensors that monitor gait motion conditions may need to be attached to the insoles, in closer proximity to the foot of users. In order to investigate the problem of capturing power parasitically from normal human-body-motion for use in personal electronics applications, we also plan to develop an electromechanical generator embedded within the shoe for parasitic power collection from heel strike. / Next, we can encode specific motions to control external devices through a wireless interface. This same system architecture that allows us to classify broad categories of motion also allows the intelligent shoe to act as a programmable, low-data rate control interface. We apply the system to several successful tasks based on this platform, especially the Shoe-Mouse. By using this interface, we can operate a device with our feet. / Then, we present potential use of machine learning techniques, in particular support vector machine (SVM), and the intelligent shoe platform to detect discrete stages in the cyclic motion of dynamic human gait, and construct an identifier of five discrete events that occur in a cyclic process for precise control of functional electrical stimulation (FES). With the information of when the legs are in each phase of a gait, the timing of specific gait phase can be assessed. / Huang, Bufu. / "September 2007." / Adviser: Yangsheng Xu. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4931. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 122-131). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 58 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 55-58).

Thesis (M.A. in Security Studies (Homeland Security and Defense))--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Dahl, Erik J. ; Denning, Dorothy E. "December 2009." Description based on title screen as viewed on January 26, 2010. Author(s) subject terms: REAL ID, PASS ID, biometrics, driver's license, enhanced driver's license, national identification card, biometric technologies, fingerprints, iris scan, facial recognition, hand geometry, Department of Homeland Security. Includes bibliographical references (p. 85-96). Also available in print.

Thesis (Ph.D.)--City University of Hong Kong, 2009. / "Submitted to the Department of Electronic Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references.