Handbook of Fingerprint Recognition

Preface
Overview
Objectives
Organization and Features
From the Second to the Third Edition
Contents of the Electronic Supplementary Material (ESM)
Intended Audience
Contents
Acronyms
1 Introduction
1.1 Introduction
1.2 Biometric Recognition
1.3 Biometric Systems
1.4 Comparison of Traits
1.5 System Errors
1.5.1 Reasons Behind System Errors
1.5.2 Capture Module Errors
1.5.3 Feature Extraction Module Errors
1.5.4 Template Creation Module Errors
1.5.5 Matching Module Errors
1.5.6 Verification Error Rates
1.5.7 Identification Error Rates
1.5.8 Presentation Attack Detection Errors
1.6 System Evaluation
1.7 Applications of Fingerprint Systems
1.7.1 Application Characteristics
1.7.2 Application Categories
1.7.3 Barriers to Adoption
1.8 History of Fingerprints
1.9 Formation of Fingerprints
1.10 Individuality and Persistence of Fingerprints
1.11 Fingerprint Sensing
1.12 Fingerprint Representation and Feature Extraction
1.13 Fingerprint Matching
1.14 Fingerprint Classification and Indexing
1.15 Latent Fingerprint Recognition
1.16 Synthetic Fingerprints
1.17 Biometric Fusion
1.18 System Integration and Administration Issues
1.19 Securing Fingerprint Systems
1.20 Privacy Issues
1.21 Summary and Future Prospects
1.22 Image Processing, Pattern Recognition, and Machine Learning Background
1.22.1 Image Processing Books
1.22.2 Pattern Recognition and Machine Learning Books
1.22.3 Journals
References
2 Fingerprint Sensing
2.1 Introduction
2.2 Off-Line Fingerprint Acquisition
2.3 Live-Scan Fingerprint Sensing
2.3.1 Optical Sensors
2.3.2 Capacitive Sensors
2.3.3 Thermal Sensors
2.3.4 Pressure Sensors
2.3.5 Ultrasound Sensors
2.4 Swipe Sensors
2.5 Fingerprint Images and Their Parameters
2.6 Image Quality Specifications for Fingerprint Scanners
2.7 Operational Quality of Fingerprint Scanners
2.8 Examples of Fingerprint Scanners
2.9 Dealing with Small-Area Sensors
2.10 Storing and Compressing Fingerprint Images
2.11 Summary
References
3 Fingerprint Analysis and Representation
3.1 Introduction
3.2 Segmentation
3.2.1 Segmentation Based on Handcrafted Features and Thresholding
3.2.2 Learning-Based Segmentation with Simple Classifiers
3.2.3 Total Variation Models
3.2.4 Deep Learning Models
3.3 Local Ridge Orientation Estimation
3.3.1 Gradient-Based Approaches
3.3.2 Slit- and Projection-Based Approaches
3.3.3 Orientation Estimation in the Frequency Domain
3.3.4 Orientation Image Regularization
3.3.5 Global Models of Ridge Orientations
3.3.6 Learning-Based Methods
3.3.7 Benchmarking Fingerprint Orientation Extraction
3.4 Local Ridge Frequency Estimation
3.5 Singularity Detection and Pose Estimation
3.5.1 Poincaré
3.5.2 Methods Based on Local Characteristics of the Orientation Image
3.5.3 Partitioning-Based Methods
3.5.4 Methods Based on a Global Model of the Orientation Image
3.5.5 Fingerprint Pose Estimation
3.6 Enhancement
3.6.1 Pixel-Wise Enhancement
3.6.2 Contextual Filtering
3.6.3 Multi-Resolution and Iterative Enhancement
3.6.4 Learning-Based Enhancement
3.6.5 Crease Detection and Removal
3.7 Minutiae Detection
3.7.1 Binarization-Based Methods
3.7.2 Direct Gray-Scale Extraction
3.7.3 Learning-Based Approaches
3.7.4 Minutiae Encoding Standards
3.7.5 Benchmarking Minutiae Extraction
3.8 Minutiae Filtering
3.8.1 Structural Post-Processing
3.8.2 Minutiae Filtering in the Gray-Scale Domain
3.9 Estimation of Ridge Count
3.10 Pore Detection
3.10.1 Skeletonization
3.10.2 Filtering
3.10.3 Topological Approaches
3.10.4 Deep Learning Methods
3.11 Estimation of Fingerprint Quality
3.11.1 Local Quality Estimation
3.11.2 Global Quality Estimation
3.11.3 NFIQ (NIST Fingerprint Image Quality)
3.12 Summary
References
4 Fingerprint Matching
4.1 Introduction
4.2 Correlation-Based Techniques
4.3 Minutiae-Based Methods
4.3.1 Problem Formulation
4.3.2 Similarity Score
4.3.3 Global Minutiae Matching Approaches
4.3.4 Hough Transform-Based Approaches
4.3.5 Consensus-Based Approaches
4.3.6 Spectral Minutiae Representation
4.3.7 Minutiae Matching with Pre-Alignment
4.4 Global Versus Local Minutiae Matching
4.4.1 Archetype Methods for Nearest Neighbor-Based and Fixed Radius-Based Local Minutiae Structures
4.4.2 Evolution of Local Structure Matching
4.4.3 Minutiae Cylinder Code
4.4.4 Consolidation
4.5 Dealing with Distortion
4.5.1 Fingerprint Distortion Models
4.5.2 Tolerance Box Adaptation
4.5.3 Warping
4.5.4 Dense Registration
4.5.5 A-Priori Distortion Removal
4.6 Feature-Based Matching Techniques
4.6.1 Early Global Methods
4.6.2 Local Orientation and Frequencies
4.6.3 Geometrical Attributes and Spatial Relationship of the Ridge Lines
4.6.4 Handcrafted Textural Features
4.6.5 Deep Features
4.6.6 Pore Matching
4.7 Comparing the Performance of Matching Algorithms
4.7.1 Fingerprint Databases
4.7.2 Fingerprint Evaluation Campaigns
4.7.3 Interoperability of Fingerprint Recognition Algorithms
4.8 Summary
References
5 Fingerprint Classification and Indexing
5.1 Introduction
5.2 Classification
5.2.1 Rule-Based Approaches
5.2.2 Syntactic Approaches
5.2.3 Structural Approaches
5.2.4 Statistical Approaches
5.2.5 Neural Network-Based Approaches
5.2.6 Multiple Classifier-Based Approaches
5.2.7 Fingerprint Sub-Classification
5.3 Benchmarking Fingerprint Classification Techniques
5.3.1 Metrics
5.3.2 Datasets
5.3.3 Search Strategies for Exclusive Classification
5.4 Fingerprint Indexing and Retrieval
5.4.1 Methods Based on Orientation and Frequency Images
5.4.2 Methods Based on Matching Scores
5.4.3 Methods Based on Minutiae
5.4.4 Hybrid and Ensemble Methods
5.4.5 Deep Learning-Based Methods
5.5 Benchmarking Fingerprint Indexing Techniques
5.5.1 Metrics and Benchmarks
5.5.2 Comparison of Existing Approaches
5.6 Summary
References
6 Latent Fingerprint Recognition
6.1 Introduction
6.2 Latent Fingerprint Recognition by Latent Examiners
6.2.1 ACE-V
6.2.2 Criticisms
6.2.3 Recent Advances
6.3 Automated Latent Fingerprint Recognition
6.4 Feature Extraction
6.4.1 Challenges
6.4.2 Pose Estimation
6.4.3 Foreground Segmentation
6.4.4 Local Ridge Orientation Estimation
6.4.5 Overlapping Fingerprint Separation
6.4.6 Ridge Enhancement and Minutiae Detection
6.4.7 Quality Estimation
6.5 Matching
6.5.1 Challenges
6.5.2 Latent Matching with Manually Marked Features
6.5.3 Latent Matching with Automatically Extracted Features
6.5.4 Performance Evaluation
6.6 Summary
References
7 Fingerprint Synthesis
7.1 Introduction
7.2 Generation of a Master Fingerprint
7.2.1 Fingerprint Area Generation
7.2.2 Orientation Image Generation
7.2.3 Frequency Image Generation
7.2.4 Ridge Pattern Generation
7.3 Generation of Fingerprints from a Master Fingerprint
7.3.1 Variation in Ridge Thickness
7.3.2 Fingerprint Distortion
7.3.3 Perturbation and Rendering
7.3.4 Background Generation
7.4 Direct Generation of Synthetic Fingerprints
7.5 Validation of Synthetic Generators
7.5.1 Ranking Difference Among Comparison Algorithms
7.5.2 Match Score Distributions
7.5.3 Fingerprint Quality Measures
7.5.4 Minutiae Histograms
7.5.5 Analysis of Multiple Features
7.5.6 Large Scale Experiments
7.6 The “SFinGe” Software
7.7 Summary
References
8 Fingerprint Individuality
8.1 Introduction
8.2 Theoretical Approach
8.2.1 Early Individuality Models
8.2.2 Uniform Minutiae Placement Model
8.2.3 Other Models
8.3 Empirical Approach
8.4 Persistence of Fingerprints
8.5 Summary
References
9 Securing Fingerprint Systems
9.1 Introduction
9.2 Threat Model for Fingerprint Systems
9.2.1 Insider Attacks
9.2.2 External Adversarial Attacks
9.3 Methods of Obtaining Fingerprint Data and Countermeasures
9.3.1 Lifting Latent Fingerprints
9.3.2 Extracting Fingerprints from High-Resolution Photos
9.3.3 Guessing Fingerprint Data by Hill Climbing
9.3.4 Stealing Fingerprint Data from the Template Database
9.3.5 Countermeasures for Protecting Fingerprint Data
9.4 Presentation Attacks
9.4.1 Fingerprint Spoofs
9.4.2 Altered Fingerprints
9.5 Presentation Attack Detection
9.5.1 Hardware-Based Approaches for Spoof Detection
9.5.2 Software-Based Approaches for Spoof Detection
9.5.3 Altered Fingerprint Detection
9.5.4 PAD Performance Evaluation
9.5.5 Challenges and Open Issues
9.6 Template Protection
9.6.1 Desired Characteristics
9.6.2 Template Protection Approaches
9.6.3 Feature Transformation
9.6.4 Fingerprint Cryptosystems
9.6.5 Feature Adaptation
9.6.6 Challenges and Open Issues
9.7 Building a Closed Fingerprint System
9.8 Summary
References