Table of Contents: Handbook of Vascular Biometrics.

Handbook of Vascular Biometrics.

Bibliographic Details
Main Author:	Uhl, Andreas.
Other Authors:	Busch, Christoph., Marcel, Sébastien., Veldhuis, Raymond.
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing AG, 2019.
Edition:	1st ed.
Series:	Advances in Computer Vision and Pattern Recognition Series
Subjects:	Electronic books.
Online Access:	Click to View

Table of Contents:

Intro
Foreword
Preface
Objectives
Audience
Organisation
Part I: Introduction
Part II: Hand and Finger Vein Biometrics
Part III: Sclera and Retina Biometrics
Part IV: Security and Privacy in Vascular Biometrics
Acknowledgements
Contents
Part I Introduction
1 State of the Art in Vascular Biometrics
1.1 Introduction
1.1.1 Imaging Hand-Based Vascular Biometric Traits
1.1.2 Imaging Eye-Based Vascular Biometric Traits
1.1.3 Pros and Cons of Vascular Biometric Traits
1.2 Commercial Sensors and Systems
1.2.1 Hand-Based Vascular Traits
1.2.2 Eye-Based Vascular Traits
1.3 Algorithms in the Recognition Toolchain
1.3.1 Finger Vein Recognition Toolchain
1.3.2 Palm Vein Recognition Toolchain
1.3.3 (Dorsal) Hand Vein Recognition Toolchain
1.3.4 Wrist Vein Recognition Toolchain
1.3.5 Retina Recognition Toolchain
1.3.6 Sclera Recognition Toolchain
1.4 Datasets, Competitions and Open-Source Software
1.4.1 Hand-Based Vascular Traits
1.4.2 Eye-Based Vascular Traits
1.5 Template Protection
1.5.1 Hand-Based Vascular Traits
1.5.2 Eye-Based Vascular Traits
1.6 Presentation Attacks and Detection, and Sample Quality
1.6.1 Presentation Attack Detection
1.6.2 Biometric Sample Quality-Hand-Based Vascular Traits
1.6.3 Biometric Sample Quality-Eye-Based Vascular Traits
1.7 Mobile and On-the-Move Acquisition
1.7.1 Hand-Based Vascular Traits
1.7.2 Eye-Based Vascular Traits
1.8 Disease Impact on Recognition and (Template) Privacy
1.9 Conclusion and Outlook
References
2 A High-Quality Finger Vein Dataset Collected Using a Custom-Designed Capture Device
2.1 Introduction
2.2 Overview of Finger Vein Acquisition Systems
2.2.1 Types of Sensors
2.2.2 Commercial Sensors
2.2.3 Sensors Developed by Academics.
2.3 University of Twente Finger Vein Capture Device
2.4 Description of Dataset
2.5 Results
2.5.1 Performance Analysis
2.6 Next-Generation Finger Vein Scanner
2.6.1 Overview
2.6.2 Illumination Control
2.6.3 3D Reconstruction
2.7 Conclusions
2.8 Future Work
References
3 OpenVein-An Open-Source Modular Multipurpose Finger Vein Scanner Design
3.1 Introduction
3.2 Finger Vein Scanners
3.2.1 Light Source Positioning
3.2.2 Two Main Perspectives of the Finger-Dorsal and Palmar
3.2.3 Commercial Finger Vein Scanners
3.2.4 Finger Vein Prototype Scanners and Datasets in Research
3.3 PLUS OpenVein Finger Vein Scanner
3.3.1 Advantages and Differences to Existing Designs
3.3.2 Image Sensor, Lens and Additional Filter
3.3.3 Light Transmission Illuminator
3.3.4 Reflected Light Illuminator
3.3.5 Illuminator Brightness Control Board
3.3.6 Finger Placement Unit
3.3.7 Housing Parts
3.3.8 Capturing Software
3.4 PLUSVein-FV3 Finger Vein Dataset
3.5 Conclusion
3.5.1 Future Work
References
4 An Available Open-Source Vein Recognition Framework
4.1 Introduction
4.2 Related Work
4.3 PLUS OpenVein Toolkit
4.3.1 Directory Structure
4.3.2 Settings Files
4.3.3 External Dependencies
4.4 Included Vein Recognition Schemes
4.4.1 Input File Handling/Supported Datasets
4.4.2 Preprocessing
4.4.3 Feature Extraction
4.4.4 Comparison
4.4.5 Comparison/Evaluation Protocols
4.4.6 Performance Evaluation Tools
4.4.7 Feature and Score-Level Fusion
4.5 Experimental Example
4.5.1 Dataset and Experimental Set-Up
4.5.2 Experimental Results
4.6 Conclusion and Future Work
References
Part II Hand and Finger Vein Biometrics
5 Use Case of Palm Vein Authentication
5.1 Introduction
5.2 Palm Vein Sensing
5.3 Sensor Products with Reflection Method.
5.4 Matching Performance
5.5 Use Cases of Palm Vein Authentication
5.5.1 Usage Situation
5.5.2 Login Authentication
5.5.3 Physical Access Control Systems
5.5.4 Payment Systems
5.5.5 Financial Services
5.5.6 Health Care
5.5.7 Airport Security
5.5.8 Government and Municipal
5.6 Conclusion
References
6 Evolution of Finger Vein Biometric Devices in Terms of Usability
6.1 Introduction
6.1.1 Early Implementation
6.1.2 Commercialisation
6.1.3 Evolutions of the Finger Vein Biometric Devices
6.2 Compliance with Regulations
6.2.1 Use Case/Background
6.2.2 Usability Requirement Details
6.2.3 Challenges
6.2.4 Implementation
6.3 Compactness
6.3.1 Use Case/Background
6.3.2 Usability Requirement Details
6.3.3 Challenges
6.3.4 Implementation
6.4 Portability and Mobility
6.4.1 Use Case/Background
6.4.2 Usability Requirement Details
6.4.3 Challenges
6.4.4 Implementation
6.5 Universal Design
6.5.1 Use Case/Background
6.5.2 Usability Requirement Details
6.5.3 Challenges
6.5.4 Implementation
6.6 Durability and Anti-vandalism
6.6.1 Use Case/Background
6.6.2 Usability Requirement Details
6.6.3 Challenges
6.6.4 Implementation
6.7 High Throughput
6.7.1 Use Case/Background
6.7.2 Usability Requirement Details
6.7.3 Challenges
6.7.4 Implementation
6.8 Universality/Availability
6.8.1 Use Case/Background
6.8.2 Usability Requirement Details
6.8.3 Challenges
6.8.4 Implementation
6.9 Summary
References
7 Towards Understanding Acquisition Conditions Influencing Finger Vein Recognition
7.1 Introduction
7.2 Varying Acquisition Conditions-A Challenging Aspect in Research and Practical Applications
7.3 Deployed Scanner Devices
7.4 Finger Vein Acquisition Conditions Dataset.
7.5 Finger Vein Recognition Toolchain and Evaluation Protocol
7.6 Experimental Results Analysis
7.7 Conclusion
References
8 Improved CNN-Segmentation-Based Finger Vein Recognition Using Automatically Generated and Fused Training Labels
8.1 Introduction
8.2 Related Works
8.2.1 Classical Finger Vein Recognition Techniques
8.2.2 CNN-Based Finger Vein Recognition
8.2.3 Automated Generation of CNN Training Data
8.3 Finger Vein Pattern Extraction Using CNNs
8.4 Training Label Generation and Setups
8.5 Experimental Framework
8.6 Results
8.7 Discussion
8.8 Conclusion
References
9 Efficient Identification in Large-Scale Vein Recognition Systems Using Spectral Minutiae Representations
9.1 Introduction
9.1.1 Organisation
9.1.2 Workload Reduction in Vein Identification Systems
9.1.3 Concept Focus
9.2 Workload Reduction Concepts
9.2.1 Efficient Data Representation
9.2.2 Serial Combination of SMR
9.2.3 Indexing Methods
9.2.4 Hardware Acceleration
9.2.5 Fusion of Concepts
9.3 Experiments
9.3.1 Experimental Setup
9.3.2 Performance Evaluation
9.3.3 Experiments Overview
9.4 Results
9.4.1 Spectral Minutiae Representation
9.4.2 Binary Spectral Minutiae Representation
9.4.3 Serial Combination of SMR
9.4.4 Indexing Methods
9.4.5 Fusion of Concepts
9.4.6 Discussion
9.5 Summary
References
10 Different Views on the Finger
- Score-Level Fusion in Multi-Perspective Finger Vein Recognition
10.1 Introduction
10.2 Multi-perspective Finger Vein Biometrics
10.3 Multi-perspective Finger Vein Capture Device
10.4 Multi-perspective Finger Vein Dataset
10.5 Biometric Fusion
10.5.1 Fusion in Finger Vein Recognition
10.6 Experimental Analysis
10.6.1 Finger Vein Dataset
10.6.2 Finger Vein Recognition Tool chain.
10.6.3 Score-Level Fusion Strategy and Toolkit
10.6.4 Evaluation Protocol
10.6.5 Single Perspective Performance Results
10.6.6 Multi-perspective Fusion Results
10.6.7 Multi-algorithm Fusion Results
10.6.8 Combined Multi-perspective and Multi-algorithm Fusion
10.6.9 Results Discussion
10.7 Conclusion and Future Work
References
Part III Sclera and Retina Biometrics
11 Retinal Vascular Characteristics
11.1 Introduction
11.1.1 Anatomy of the Retina
11.1.2 History of Retinal Recognition
11.1.3 Medical and Biometric Examination and Acquisition Tools
11.1.4 Recognition Schemes
11.1.5 Achieved Results Using Our Scheme
11.1.6 Limitations
11.2 Eye Diseases
11.2.1 Automatic Detection of Druses and Exudates
11.2.2 Testing
11.3 Biometric Information Amounts in the Retina
11.3.1 Theoretical Determination of Biometric Information in Retina
11.3.2 Used Databases and Applications
11.3.3 Results
11.4 Synthetic Retinal Images
11.4.1 Vascular Bed Layer
11.4.2 Layers
11.4.3 Background Layers
11.4.4 Generating a Vascular Bed
11.4.5 Testing
11.4.6 Generating Synthetic Images Via Neural Network
References
12 Vascular Biometric Graph Comparison: Theory and Performance
12.1 Introduction
12.2 The Biometric Graph
12.2.1 The Biometric Graph
12.2.2 Biometric Graph Extraction
12.3 The Biometric Graph Comparison Algorithm
12.3.1 BGR-Biometric Graph Registration
12.3.2 BGC-Biometric Graph Comparison
12.4 Results
12.4.1 Vascular Databases
12.4.2 Comparison of Graph Topology Across Databases
12.4.3 Comparison of MCS Topology in BGC
12.4.4 Comparison of BGC Performance Across Databases
12.5 Anchors for a BGC Approach to Template Protection
12.5.1 Dissimilarity Vector Templates for Biometric Graphs
12.5.2 Anchors for Registration.
12.5.3 The Search for Anchors.

Handbook of Vascular Biometrics.

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