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Biometric Authentication: A Machine Learning Approach

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Biometric Authentication: A Machine Learning Approach

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  • Copyright 2005
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-147824-9
  • ISBN-13: 978-0-13-147824-4

  • A breakthrough approach to improving biometrics performance
  • Constructing robust information processing systems for face and voice recognition
  • Supporting high-performance data fusion in multimodal systems
  • Algorithms, implementation techniques, and application examples

Machine learning: driving significant improvements in biometric performance

As they improve, biometric authentication systems are becoming increasingly indispensable for protecting life and property. This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application domains.

Three leading researchers bridge the gap between research, design, and deployment, introducing key algorithms as well as practical implementation techniques. They demonstrate how to construct robust information processing systems for biometric authentication in both face and voice recognition systems, and to support data fusion in multimodal systems.

Coverage includes:

  • How machine learning approaches differ from conventional template matching
  • Theoretical pillars of machine learning for complex pattern recognition and classification
  • Expectation-maximization (EM) algorithms and support vector machines (SVM)
  • Multi-layer learning models and back-propagation (BP) algorithms
  • Probabilistic decision-based neural networks (PDNNs) for face biometrics
  • Flexible structural frameworks for incorporating machine learning subsystems in biometric applications
  • Hierarchical mixture of experts and inter-class learning strategies based on class-based modular networks
  • Multi-cue data fusion techniques that integrate face and voice recognition
  • Application case studies


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Expectation-Maximization Theory

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Table of Contents

Preface.

1. Overview.

    Introduction.

    Biometric Authentication Methods.

    Face Recognition: Reality and Challenge.

    Speaker Recognition: Reality and Challenge.

    Road Map of the Book.

2. Biometric Authentication Systems.

    Introduction.

    Design Tradeoffs.

    Feature Extraction.

    Adaptive Classifiers.

    Visual-Based Feature Extraction and Pattern Classification.

    Audio-Based Feature Extraction and Pattern Classification.

    Concluding Remarks.

3. Expectation-Maximization Theory.

    Introduction.

    Traditional Derivation of EM.

    An Entropy Interpretation.

    Doubly-Stochastic EM.

    Concluding Remarks.

4. Support Vector Machines.

    Introduction.

    Fisher's Linear Discriminant Analysis.

    Linear SVMs: Separable Case.

    Linear SVMs: Fuzzy Separation.

    Nonlinear SVMs.

    Biometric Authentication Application Examples.

5. Multi-Layer Neural Networks.

    Introduction.

    Neuron Models.

    Multi-Layer Neural Networks.

    The Back-Propagation Algorithms.

    Two-Stage Training Algorithms.

    Genetic Algorithm for Multi-Layer Networks.

    Biometric Authentication Application Examples.

6. Modular and Hierarchical Networks.

    Introduction.

    Class-Based Modular Networks.

    Mixture-of-Experts Modular Networks.

    Hierarchical Machine Learning Models.

    Biometric Authentication Application Examples.

7. Decision-Based Neural Networks.

    Introduction.

    Basic Decision-Based Neural Networks.

    Hierarchical Design of Decision-Based Learning Models.

    Two-Class Probabilistic DBNNs.

    Multiclass Probabilistic DBNNs.

    Biometric Authentication Application Examples.

8. Biometric Authentication by Face Recognition.

    Introduction.

    Facial Feature Extraction Techniques.

    Facial Pattern Classification Techniques.

    Face Detection and Eye Localization.

    PDBNN Face Recognition System Case Study.

    Application Examples for Face Recognition Systems.

    Concluding Remarks.

9. Biometric Authentication by Voice Recognition.

    Introduction.

    Speaker Recognition.

    Kernel-Based Probabilistic Speaker Models.

    Handset and Channel Distortion.

    Blind Handset-Distortion Compensation.

    Speaker Verification Based on Articulatory Features.

    Concluding Remarks.

10. Multicue Data Fusion.

    Introduction.

    Sensor Fusion for Biometrics.

    Hierarchical Neural Networks for Sensor Fusion.

        Multisample Fusion.

    Audio and Visual Biometric Authentication.

    Concluding Remarks.

Appendix A. Convergence Properties of EM.

Appendix B. Average DET Curves.

Appendix C. Matlab Projects.

    Matlab Project 1: GMMs and RBF Networks for Speech Pattern Recognition.

    Matlab Project 2: SVMs for Pattern Classification.

Bibliography.

Index.

Preface

Untitled Document

Biometrics has long been an active research field, particularly because of all the attention focused on public and private security systems in recent years. Advances in digital computers, software technologies, and embedded systems have further catalyzed increased interest in commercially available biometric application systems. Biometric authentication can be regarded as a special technical area in the field of pattern classification. Research and development on biometric authentication have focused on two separate fronts: one covering the theoretical aspect of machine learning for pattern classification and the other covering system design and deployment issues of biometric systems. This book is meant to bridge the gap between these two fronts, with a special emphasis on the promising roles of modern machine learning and neural network techniques.

To develop an effective biometric authentication system, it is vital to acquire a thorough understanding of the input feature space , then develop proper mapping of such feature space onto the expert space and eventually onto the output classification space . Unlike the conventional template matching approach, in which learning amounts to storing representative example patterns of a class, the machine learning approach adopts representative statistical models to capture the characteristics of patterns in the feature domain. This book explores the rich synergy between various machine learning models from the perspective of biometric applications. Practically, the machine learning models can be adopted to construct a robust information processing system for biometric authentication and data fusion. It is potentially useful in a broad spectrum of application domains, including but not limited to biometric authentication.

The book is organized into four related parts.

  • Part I--Chapters 1 and 2--provides an overview of the state-of-the-art in face and speaker biometrics authentication systems.
  • Part II--Chapters 3, 4, and 5--establishes the theoretical pillars of machine learning methods adopted in the book. To facilitate the development of effective biometric authentication systems, several modern machine learning models are instrumental in handling complex pattern recognition and classification problems. Part II discusses the expectation-maximization (EM) algorithm (Chapter 3); describes the fundamental theory on Fisher's linear discriminant analysis (LDA) and support vector machines (SVM) (Chapter 4); and offers comprehensive coverage of multi-layer learning models, in addition to well-known back-propagation (BP) algorithms (Chapter 5).
  • Part III--Chapters 6 and 7--proposes several flexible structural frameworks based on hierarchical and modular neural networks, under which machine learning modules can be incorporated as subsystems. The discussion introduces several expert-based modular networks such as the so-called hierarchical mixture-of-experts (Chapter 6) as well as interclass learning strategies based on class-based modular networks (Chapter 7).
  • Part IV--Chapters 8, 9, and 10--presents the theoretical foundations behind the learning networks, which can find natural and fruitful applications in biometric authentication systems. The most important authentication application domains are face recognition and speaker verification. Specifically, Chapter 8 presents probabilistic neural networks for face biometrics, while Chapter 9 covers authentication by human voices. Several multicue data-fusion techniques are addressed in Chapter 10.

As suggested by the title, the book covers two main themes: (1) biometric authentication and (2) the machine learning approach. The ultimate objective is to demonstrate how machine learning models can be integrated into a unified and intelligent recognition system for biometric authentication. However, the authors must admit the book's coverage is far from being comprehensive enough to do justice to either theme. First, the book does not address many important biometric authentication techniques such as signature, fingerprint, iris pattern, palm, DNA, and so on.

The focus is placed strictly on visual recognition of faces and audio verification of speakers. Due to space constraints, the book has likewise overlooked many promising machine learning models. To those numerous contributors who deserve many more credits than are given here, the authors wish to express their most sincere apologies.

In closing, Biometric Authentication: A Machine Learning Approach is intended for one-semester graduate school courses in machine learning, neural networks, and biometrics. It is also intended for professional engineers, scientists, and system integrators who want to learn systematic, practical ways of implementing computationally intelligent authentication systems based on the human face and voice.


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