Table of Contents

Title Page
Copyright Page
Preface
Table of Figures
List of Tables
Chapter 1 - Fundamentals of Biometric Technology
1.1 Biometric Authentication Technology
1.2 Some Major Biometric Applications
1.3 Operational Process of Biometric Technology
1.4 Biometric Data Indexing
1.5 Metrics for Performance Measure
1.6 Biometric Modalities
1.6.1 Iris Biometric
1.6.2 Fingerprint Biometric
1.6.3 Face Biometric
1.6.4 Palmprint Biometric
1.6.5 Hand Geometry Biometric
1.6.6 Voice Biometric
1.6.7 Gait Biometric
1.6.8 Signature Biometric
1.7 Comparative Study of Different Biometric Modalities
1.7.1 Identification of Parameters
1.7.2 Estimation of Values of Parameters
1.7.3 Estimation of Impact Value
1.7.4 Quantitative Comparison
1.8 Summary
Bibliography
Chapter 2 - Multimodal Biometric and Fusion Technology
2.1 Multimodal Biometric Authentication Technology
2.2 Fusion of Multimodalities
2.3 Fusion Levels
2.3.1 Sensor Level Fusion
2.3.2 Feature Level Fusion
2.3.3 Match-score Level Fusion
2.3.4 Decision Level Fusion
2.4 Different Fusion Rules
2.4.1 Fixed fusion rules
2.4.1.1 AND Rule
2.4.1.2 OR Rule
2.4.1.3 Majority Voting
2.4.1.4 Maximum Rule
2.4.1.5 Minimum Rule
2.4.1.6 Sum Rule
2.4.1.7 Product Rule
2.4.1.8 Arithmetic Mean Rule
2.4.2 Trained Fusion Rules
2.4.2.1 Weighted Sum Rule
2.4.2.2 Weighted Product Rule
2.4.2.3 User Weighting
2.4.2.4 Fisher Linear Discriminant (FLD)
2.4.2.5 Support Vector Machine (SVM)
2.4.2.6 Multi Layer Perceptron (MLP)
2.4.2.7 Mixture-of-Experts (MOE)
2.4.2.8 Bimodal Fusion (BMF)
2.4.2.9 Cross-Modal Fusion
2.4.2.10 3-D Multimodal Fusion
2.4.2.11 Canonical Correlation Analysis (CCA), and Kernel Canonical Correlation Analysis (KCCA)
2.4.2.12 Simple and Weighted Average
2.4.2.13 Optimal Weighting Method (OWM)
2.4.2.14 Likelihood Ratio-Based Biometric Score Fusion
2.4.2.15 Borda Count Method
2.4.2.16 Logistic Regression Method
2.4.2.17 Kernel Fischer Discriminant Analysis (KFDA)
2.4.2.18 Minimum Cost Bayesian Classifier
2.4.2.19 Decision Tree
2.5 Comparative Study of Fusion Rule
2.6 Summary
Bibliography
Chapter 3 - Biometric Indexing: State-of-the-Art
3.1 Survey on Iris Biometric Data Indexing
3.1.1 Iris Texture-Based Indexing
3.1.2 Iris Color-Based Indexing
3.2 Survey on Fingerprint Biometric Data Indexing
3.2.1 Minutiae-Based Indexing
3.2.2 Ridge Orientation-Based Indexing
3.2.3 Other Feature-Based Indexing Techniques
3.3 Survey on Face Biometric Data Indexing
3.4 Survey on Multimodal Biometric Data Indexing
3.5 Summary
Bibliography
Chapter 4 - Iris Biometric Data Indexing
4.1 Preliminaries of Gabor Filter
4.2 Preprocessing
4.3 Feature Extraction
4.4 Index Key Generation
4.5 Storing
4.5.1 Index Space Creation
4.5.2 Storing Iris Data
4.6 Retrieving
4.7 Performance Evaluation
4.7.1 Performance Metrics
4.7.2 Databases
4.7.3 Evaluation Setup
4.7.4 Validation of the Parameter Values
4.7.4.1 Values of S and K
4.7.4.2 Value of δ
4.7.5 Evaluation
4.7.5.1 Accuracy
4.7.5.2 Searching Time
4.7.5.3 Memory Requirement
4.8 Comparison with Existing Work
4.9 Summary
Bibliography
Chapter 5 - Fingerprint Biometric Data Indexing
5.1 Preprocessing
5.1.1 Normalization
5.1.2 Segmentation
5.1.3 Local Orientation Estimation
5.1.4 Local Frequency Image Representation
5.1.5 Ridge Filtering
5.1.6 Binarization and Thinning
5.1.7 Minutiae Point Extraction
5.2 Feature Extraction
5.2.1 Two Closest Points Triangulation
5.2.2 Triplet Generation
5.3 Index Key Generation
5.4 Storing
5.4.1 Linear Index Space
5.4.2 Clustered Index Space
5.4.3 Clustered kd-tree Index Space
5.5 Retrieving
5.5.1 Linear Search (LS)
5.5.2 Clustered Search (CS)
5.5.3 Clustered kd-tree Search (CKS)
5.6 Performance Evaluation
5.6.1 Databases
5.6.2 Evaluation Setup
5.6.3 Evaluation
5.6.3.1 Accuracy
5.6.4 Searching Time
5.6.5 Memory Requirements
5.7 Comparison with Existing Work
5.8 Summary
Bibliography
Chapter 6 - Face Biometric Data Indexing
6.1 Preprocessing
6.1.1 Geometric Normalization
6.1.2 Face Masking
6.1.3 Intensity Enhancement
6.2 Feature Extraction
6.2.1 Key Point Detection
6.2.1.1 Scale Space Creation
6.2.1.2 Hessian Matrix Creation
6.2.1.3 Key Point Localization
6.2.2 Orientation Assignment
6.2.3 Key Point Descriptor Extraction
6.3 Index Key Generation
6.4 Storing
6.4.1 Index Space Creation
6.4.2 Linear Storing Structure
6.4.3 Kd-tree Storing Structure
6.5 Retrieving
6.5.1 Linear Search
6.5.2 Kd-tree Search
6.6 Performance Evaluation
6.6.1 Database
6.6.2 Evaluation Setup
6.6.3 Validation of the Parameter Value
6.6.4 Evaluation
6.6.4.1 Accuracy
6.6.4.2 Searching Time
6.6.4.3 Memory Requirement
6.7 Comparison with Existing Work
6.8 Summary
Bibliography
Chapter 7 - Multimodal Biometric Data Indexing
7.1 Feature Extraction
7.2 Score Calculation
7.3 Reference Subject Selection
7.3.1 Sample Selection
7.3.2 Subject Selection
7.4 Reference Score Calculation
7.5 Score Level Fusion
7.5.1 Score Normalization
7.5.2 Score Fusion
7.6 Index Key Generation
7.7 Storing
7.7.1 Index Space Creation
7.7.2 Storing Multimodal Biometric Data
7.8 Retrieving
7.9 Rank Level Fusion
7.9.1 Creating Feature Vector for Ranking
7.9.2 SVM Ranking
7.10 Performance Evaluation
7.10.1 Database
7.10.2 Evaluation Setup
7.10.3 Training of SVM-based Score Fusion Module
7.10.4 Training of SVM-based Ranking Module
7.10.5 Validation of the Parameter Values
7.10.5.1 Number of Reference Subjects (M)
7.10.5.2 Size of Table (LB)
7.10.5.3 Number of Neighbor Cells (δ)
7.10.6 Evaluation
7.10.6.1 Accuracy
7.10.6.2 Searching Time
7.10.6.3 Memory Requirement
7.11 Comparison with Existing Work
7.12 Summary
Bibliography
Chapter 8 - Conclusions and Future Research
8.1 Dimensionality of Index Key Vector
8.2 Storing and Retrieving
8.3 Performance of Indexing Techniques
8.4 Threats to Validity
8.4.1 Internal Validity
8.4.2 External Validity
8.4.3 Construct Validity
8.5 Future Scope of Work
Bibliography
Index
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