Unimodal and Multimodal Biometric Data Indexing

Unimodal and Multimodal Biometric Data Indexing

by Somnath Dey, Debasis Samanta
Released July 2014
Publisher(s): De Gruyter
ISBN: 9781614518556

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Book description

This work is on biometric data indexing for large-scale identification systems with a focus on different biometrics data indexing methods.  It provides state-of-the-art coverage including different biometric traits, together with the pros and cons for each.  Discussion of different multimodal fusion strategies are also included.

Table of contents

  1. Title Page
  2. Copyright Page
  3. Preface
  4. Table of Contents
  5. Table of Figures
  6. List of Tables
  7. Chapter 1 - Fundamentals of Biometric Technology
    1. 1.1 Biometric Authentication Technology
    2. 1.2 Some Major Biometric Applications
    3. 1.3 Operational Process of Biometric Technology
    4. 1.4 Biometric Data Indexing
    5. 1.5 Metrics for Performance Measure
    6. 1.6 Biometric Modalities
      1. 1.6.1 Iris Biometric
      2. 1.6.2 Fingerprint Biometric
      3. 1.6.3 Face Biometric
      4. 1.6.4 Palmprint Biometric
      5. 1.6.5 Hand Geometry Biometric
      6. 1.6.6 Voice Biometric
      7. 1.6.7 Gait Biometric
      8. 1.6.8 Signature Biometric
    7. 1.7 Comparative Study of Different Biometric Modalities
      1. 1.7.1 Identification of Parameters
      2. 1.7.2 Estimation of Values of Parameters
      3. 1.7.3 Estimation of Impact Value
      4. 1.7.4 Quantitative Comparison
    8. 1.8 Summary
    9. Bibliography
  8. Chapter 2 - Multimodal Biometric and Fusion Technology
    1. 2.1 Multimodal Biometric Authentication Technology
    2. 2.2 Fusion of Multimodalities
    3. 2.3 Fusion Levels
      1. 2.3.1 Sensor Level Fusion
      2. 2.3.2 Feature Level Fusion
      3. 2.3.3 Match-score Level Fusion
      4. 2.3.4 Decision Level Fusion
    4. 2.4 Different Fusion Rules
      1. 2.4.1 Fixed fusion rules
        1. 2.4.1.1 AND Rule
        2. 2.4.1.2 OR Rule
        3. 2.4.1.3 Majority Voting
        4. 2.4.1.4 Maximum Rule
        5. 2.4.1.5 Minimum Rule
        6. 2.4.1.6 Sum Rule
        7. 2.4.1.7 Product Rule
        8. 2.4.1.8 Arithmetic Mean Rule
      2. 2.4.2 Trained Fusion Rules
        1. 2.4.2.1 Weighted Sum Rule
        2. 2.4.2.2 Weighted Product Rule
        3. 2.4.2.3 User Weighting
        4. 2.4.2.4 Fisher Linear Discriminant (FLD)
        5. 2.4.2.5 Support Vector Machine (SVM)
        6. 2.4.2.6 Multi Layer Perceptron (MLP)
        7. 2.4.2.7 Mixture-of-Experts (MOE)
        8. 2.4.2.8 Bimodal Fusion (BMF)
        9. 2.4.2.9 Cross-Modal Fusion
        10. 2.4.2.10 3-D Multimodal Fusion
        11. 2.4.2.11 Canonical Correlation Analysis (CCA), and Kernel Canonical Correlation Analysis (KCCA)
        12. 2.4.2.12 Simple and Weighted Average
        13. 2.4.2.13 Optimal Weighting Method (OWM)
        14. 2.4.2.14 Likelihood Ratio-Based Biometric Score Fusion
        15. 2.4.2.15 Borda Count Method
        16. 2.4.2.16 Logistic Regression Method
        17. 2.4.2.17 Kernel Fischer Discriminant Analysis (KFDA)
        18. 2.4.2.18 Minimum Cost Bayesian Classifier
        19. 2.4.2.19 Decision Tree
    5. 2.5 Comparative Study of Fusion Rule
    6. 2.6 Summary
    7. Bibliography
  9. Chapter 3 - Biometric Indexing: State-of-the-Art
    1. 3.1 Survey on Iris Biometric Data Indexing
      1. 3.1.1 Iris Texture-Based Indexing
      2. 3.1.2 Iris Color-Based Indexing
    2. 3.2 Survey on Fingerprint Biometric Data Indexing
      1. 3.2.1 Minutiae-Based Indexing
      2. 3.2.2 Ridge Orientation-Based Indexing
      3. 3.2.3 Other Feature-Based Indexing Techniques
    3. 3.3 Survey on Face Biometric Data Indexing
    4. 3.4 Survey on Multimodal Biometric Data Indexing
    5. 3.5 Summary
    6. Bibliography
  10. Chapter 4 - Iris Biometric Data Indexing
    1. 4.1 Preliminaries of Gabor Filter
    2. 4.2 Preprocessing
    3. 4.3 Feature Extraction
    4. 4.4 Index Key Generation
    5. 4.5 Storing
      1. 4.5.1 Index Space Creation
      2. 4.5.2 Storing Iris Data
    6. 4.6 Retrieving
    7. 4.7 Performance Evaluation
      1. 4.7.1 Performance Metrics
      2. 4.7.2 Databases
      3. 4.7.3 Evaluation Setup
      4. 4.7.4 Validation of the Parameter Values
        1. 4.7.4.1 Values of S and K
        2. 4.7.4.2 Value of δ
      5. 4.7.5 Evaluation
        1. 4.7.5.1 Accuracy
        2. 4.7.5.2 Searching Time
        3. 4.7.5.3 Memory Requirement
    8. 4.8 Comparison with Existing Work
    9. 4.9 Summary
    10. Bibliography
  11. Chapter 5 - Fingerprint Biometric Data Indexing
    1. 5.1 Preprocessing
      1. 5.1.1 Normalization
      2. 5.1.2 Segmentation
      3. 5.1.3 Local Orientation Estimation
      4. 5.1.4 Local Frequency Image Representation
      5. 5.1.5 Ridge Filtering
      6. 5.1.6 Binarization and Thinning
      7. 5.1.7 Minutiae Point Extraction
    2. 5.2 Feature Extraction
      1. 5.2.1 Two Closest Points Triangulation
      2. 5.2.2 Triplet Generation
    3. 5.3 Index Key Generation
    4. 5.4 Storing
      1. 5.4.1 Linear Index Space
      2. 5.4.2 Clustered Index Space
      3. 5.4.3 Clustered kd-tree Index Space
    5. 5.5 Retrieving
      1. 5.5.1 Linear Search (LS)
      2. 5.5.2 Clustered Search (CS)
      3. 5.5.3 Clustered kd-tree Search (CKS)
    6. 5.6 Performance Evaluation
      1. 5.6.1 Databases
      2. 5.6.2 Evaluation Setup
      3. 5.6.3 Evaluation
        1. 5.6.3.1 Accuracy
      4. 5.6.4 Searching Time
      5. 5.6.5 Memory Requirements
    7. 5.7 Comparison with Existing Work
    8. 5.8 Summary
    9. Bibliography
  12. Chapter 6 - Face Biometric Data Indexing
    1. 6.1 Preprocessing
      1. 6.1.1 Geometric Normalization
      2. 6.1.2 Face Masking
      3. 6.1.3 Intensity Enhancement
    2. 6.2 Feature Extraction
      1. 6.2.1 Key Point Detection
        1. 6.2.1.1 Scale Space Creation
        2. 6.2.1.2 Hessian Matrix Creation
        3. 6.2.1.3 Key Point Localization
      2. 6.2.2 Orientation Assignment
      3. 6.2.3 Key Point Descriptor Extraction
    3. 6.3 Index Key Generation
    4. 6.4 Storing
      1. 6.4.1 Index Space Creation
      2. 6.4.2 Linear Storing Structure
      3. 6.4.3 Kd-tree Storing Structure
    5. 6.5 Retrieving
      1. 6.5.1 Linear Search
      2. 6.5.2 Kd-tree Search
    6. 6.6 Performance Evaluation
      1. 6.6.1 Database
      2. 6.6.2 Evaluation Setup
      3. 6.6.3 Validation of the Parameter Value
      4. 6.6.4 Evaluation
        1. 6.6.4.1 Accuracy
        2. 6.6.4.2 Searching Time
        3. 6.6.4.3 Memory Requirement
    7. 6.7 Comparison with Existing Work
    8. 6.8 Summary
    9. Bibliography
  13. Chapter 7 - Multimodal Biometric Data Indexing
    1. 7.1 Feature Extraction
    2. 7.2 Score Calculation
    3. 7.3 Reference Subject Selection
      1. 7.3.1 Sample Selection
      2. 7.3.2 Subject Selection
    4. 7.4 Reference Score Calculation
    5. 7.5 Score Level Fusion
      1. 7.5.1 Score Normalization
      2. 7.5.2 Score Fusion
    6. 7.6 Index Key Generation
    7. 7.7 Storing
      1. 7.7.1 Index Space Creation
      2. 7.7.2 Storing Multimodal Biometric Data
    8. 7.8 Retrieving
    9. 7.9 Rank Level Fusion
      1. 7.9.1 Creating Feature Vector for Ranking
      2. 7.9.2 SVM Ranking
    10. 7.10 Performance Evaluation
      1. 7.10.1 Database
      2. 7.10.2 Evaluation Setup
      3. 7.10.3 Training of SVM-based Score Fusion Module
      4. 7.10.4 Training of SVM-based Ranking Module
      5. 7.10.5 Validation of the Parameter Values
        1. 7.10.5.1 Number of Reference Subjects (M)
        2. 7.10.5.2 Size of Table (LB)
        3. 7.10.5.3 Number of Neighbor Cells (δ)
      6. 7.10.6 Evaluation
        1. 7.10.6.1 Accuracy
        2. 7.10.6.2 Searching Time
        3. 7.10.6.3 Memory Requirement
    11. 7.11 Comparison with Existing Work
    12. 7.12 Summary
    13. Bibliography
  14. Chapter 8 - Conclusions and Future Research
    1. 8.1 Dimensionality of Index Key Vector
    2. 8.2 Storing and Retrieving
    3. 8.3 Performance of Indexing Techniques
    4. 8.4 Threats to Validity
      1. 8.4.1 Internal Validity
      2. 8.4.2 External Validity
      3. 8.4.3 Construct Validity
    5. 8.5 Future Scope of Work
    6. Bibliography
  15. Index

Product information

  • Title: Unimodal and Multimodal Biometric Data Indexing
  • Author(s): Somnath Dey, Debasis Samanta
  • Release date: July 2014
  • Publisher(s): De Gruyter
  • ISBN: 9781614518556