Unimodal and Multimodal Biometric Data Indexing
1st Edition
Published on 20. January 2015
XXV, 332 pages
978-1-61451-629-3 (ISBN)
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.
More details
Language
English
Place of publication
Boston
United States
Target group
Professional and scholarly
US School Grade: College Graduate Student
Illustrations
3 farbige Abbildungen, 43 s/w Tabellen, 7 s/w Abbildungen
7 b/w and 3 col. ill., 43 b/w tbl.
File size
5,28 MB
ISBN-13
978-1-61451-629-3 (9781614516293)
Schweitzer Classification
Other editions
Additional editions
Somnath Dey | Debasis Samanta
Unimodal and Multimodal Biometric Data Indexing
E-Book
07/2014
1st Edition
De Gruyter
€114.95
Available for download
Somnath Dey | Debasis Samanta
Unimodal and Multimodal Biometric Data Indexing
Book
04/2014
1st Edition
De Gruyter
€114.95
Shipment within 3-4 weeks
Persons
Somnath Dey, Indian Institute of Technology, Indore, India; Debasis Samanta, Indian Institute of Technology, Kharagpur, India.
Content
- Intro
- print_cont_9781614517459
- Contents
- List of Figures
- List of Tables
- 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
- 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
- 2.4.2 Trained Fusion Rules
- 2.5 Comparative Study of Fusion Rule
- 2.6 Summary
- 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
- 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
- 4.7.5 Evaluation
- 4.8 Comparison with Existing Work
- 4.9 Summary
- 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
- 5.6.4 Searching Time
- 5.6.5 Memory Requirements
- 5.7 Comparison with Existing Work
- 5.8 Summary
- 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
- 6.2.2 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
- 6.7 Comparison with Existing Work
- 6.8 Summary
- 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
- 7.10.6 Evaluation
- 7.11 Comparison with Existing Work
- 7.12 Summary
- 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
- Index
