The third edition of this authoritative and comprehensive handbook is the definitive work on the current state of the art of Biometric Presentation Attack Detection (PAD) – also known as Biometric Anti-Spoofing. Building on the success of the previous editions, this thoroughly updated third edition has been considerably revised to provide even greater coverage of PAD methods, spanning biometrics systems based on face, fingerprint, iris, voice, vein, and signature recognition. New material is also included on major PAD competitions, important databases for research, and on the impact of recent international legislation. Valuable insights are supplied by a selection of leading experts in the field, complete with results from reproducible research, supported by source code and further information available at an associated website.
Topics and features: reviews the latest developments in PAD for fingerprint biometrics, covering recent technologies like Vision Transformers, and review of competition series; examines methods for PAD in iris recognition systems, the use of pupil size measurement or multiple spectra for this purpose; discusses advancements in PAD methods for face recognition-based biometrics, such as recent progress on detection of 3D facial masks and the use of multiple spectra with Deep Neural Networks; presents an analysis of PAD for automatic speaker recognition (ASV), including a study of the generalization to unseen attacks; describes the results yielded by key competitions on fingerprint liveness detection, iris liveness detection, and face anti-spoofing; provides analyses of PAD in finger-vein recognition, in signature biometrics, and in mobile biometrics; includes coverage of international standards in PAD and legal aspects of image manipulations like morphing.This text/reference is essential reading for anyone involved in biometric identity verification, be they students, researchers, practitioners, engineers, or technology consultants. Those new to the field will also benefit from a number of introductory chapters, outlining the basics for the most important biometrics.
This text/reference is essential reading for anyone involved in biometric identity verification, be they students, researchers, practitioners, engineers, or technology consultants. Those new to the field will also benefit from a number of introductory chapters, outlining the basics for the most important biometrics.
Author(s): Sébastien Marcel, Julian Fierrez, Nicholas Evans
Series: Advances in Computer Vision and Pattern Recognition
Edition: 3
Publisher: Springer
Year: 2023
Language: English
Pages: 594
City: Singapore
Foreword
Preface
List of Reviewers
Contents
Contributors
Part I Fingerprint Biometrics
1 Introduction to Presentation Attack Detection in Fingerprint Biometrics
1.1 Introduction
1.2 Early Works in Fingerprint Presentation Attack Detection
1.3 A Brief View on Where We Are
1.4 Fingerprint Spoofing Databases
1.5 Conclusions
References
2 Vision Transformers for Fingerprint Presentation Attack Detection
2.1 Introduction
2.2 Related Works
2.3 Software-Based Solutions for F-PAD
2.3.1 Hand-crafted Texture-Based Solutions for F-PAD
2.3.2 Deep-Learning-Based Solutions for F-PAD
2.4 Data-Efficient Image Transformers (DeiT) for F-PAD
2.5 Databases
2.5.1 LivDet 2015 Database
2.5.2 LivDet 2019 Database
2.5.3 LivDet 2019 Database
2.6 Experiments and Results
2.6.1 Evaluation on LivDet 2015 Dataset with Multiple Known Classes (Combined Set) Training and Few Unknown Classes in Testing
2.6.2 Evaluation on LivDet 2019 Dataset with Multiple Known Classes (combined Set) in Training and All Unknown Classes in Testing
2.6.3 Analysis of Explainability
2.6.4 LivDet 2019—Impact of Limited Seen Classes During Training and Sensor Interoperability
2.6.5 Analysis of Explainability in True Unknown Data Setting
2.7 Conclusion
References
3 Review of the Fingerprint Liveness Detection (LivDet) Competition Series: From 2009 to 2021
3.1 Introduction
3.2 Fingerprint Presentation Attack Detection
3.3 The Fingerprint Liveness Detection Competition
3.4 Methods and Dataset
3.4.1 Algorithms part
3.4.2 LivDet Systems
3.4.3 Performance Evaluation
3.5 Examination of Results
3.5.1 Non Consensual Verses Consensual data
3.5.2 Materials Analysis
3.5.3 LivDet Systems Results
3.6 Conclusion
References
4 A Unified Model for Fingerprint Authentication and Presentation Attack Detection
4.1 Introduction
4.2 Related Work
4.2.1 Fingerprint Spoof Detection
4.2.2 Fingerprint Matching
4.3 Motivation
4.4 Methodology
4.4.1 DualHeadMobileNet (DHM)
4.4.2 Joint Training
4.5 Experiments and Results
4.5.1 Datasets
4.5.2 Comparison with State-of-the-art Methods
4.5.3 Time and Memory
4.6 Ablation Study
4.6.1 Effect of Varying the Split Point
4.6.2 Effect of Suppression
4.6.3 Evaluation of Matching Feature Vectors
4.6.4 Robustness to Network Architecture
4.6.5 DHR
4.6.6 DHI
4.7 Failure Cases
4.7.1 False Rejects
4.7.2 False Accepts
4.8 Implementation Details
4.9 Conclusion
References
Part II Iris Biometrics
5 Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances
5.1 Introduction
5.2 Vulnerabilities in Iris Biometrics
5.2.1 Zero-effort Attacks
5.2.2 Photo and Video Attacks
5.2.3 Contact Lens Attacks
5.2.4 Synthetic Eye Attacks
5.2.5 Cadaver Eye Attacks
5.3 Presentation Attack Detection Approaches
5.3.1 Hardware-Based Approaches
5.3.2 Software-Based Approaches
5.3.3 Challenge–Response Approaches
5.4 Integration with Iris Recognition Systems
5.5 Conclusions
References
6 Pupil Size Measurement and Application to Iris Presentation Attack Detection
6.1 Introduction
6.2 Database
6.2.1 Acquisition
6.2.2 Estimation of Pupil Size
6.2.3 Noise and Missing Data
6.2.4 Division of Data and Recognition Scenarios
6.3 Parametric Model of Pupil Dynamics
6.4 Data-Driven Models of Pupil Dynamics
6.4.1 Variants of Recurrent Neural Networks
6.4.2 Implementation and Hyperparameters
6.5 Results
6.6 Open-Hardware and Open-Source Pupil Size Measurement Device
6.6.1 Brief Characteristics
6.6.2 Related Works on Pupillometry
6.6.3 Design
6.6.4 Assembly Details
6.7 Discussion
References
7 Review of Iris Presentation Attack Detection Competitions
7.1 Introduction
7.2 Datasets
7.2.1 LivDet-Iris 2013 Data
7.2.2 LivDet-Iris 2015 Data
7.2.3 LivDet-Iris 2017 Data
7.2.4 LivDet-Iris 2020 Data
7.3 Challenges
7.4 Performance Evaluation
7.5 Summary of LivDet-Iris Results
7.5.1 Participants
7.5.2 Trends in LivDet-Iris Across All Competitions
7.6 Conclusions and Future of LivDet-Iris
References
8 Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains
8.1 Introduction
8.2 Related Works
8.3 Methodology
8.3.1 Baseline: DenseNet
8.3.2 Pixel-Wise Binary Supervision Network (PBS)
8.3.3 Attention-Based PBS Network (A-PBS)
8.3.4 Loss Function
8.3.5 Implementation Details
8.4 Experimental Evaluation
8.4.1 Databases
8.4.2 Evaluation Metrics
8.5 Intra-Spectrum and Cross-Database Evaluation Results
8.5.1 Iris PAD in the NIR Spectrum
8.5.2 Iris PAD in the Visible Spectrum
8.6 Cross-Spectrum Evaluation Results
8.7 Visualization and Explainability
8.8 Conclusion
8.9 Glossary
References
Part III Face Biometrics
9 Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances
9.1 Introduction
9.2 Vulnerabilities in Face Biometrics
9.2.1 Presentation Attack Methods
9.3 Presentation Attack Detection
9.3.1 Software-Based Face PAD
9.4 Face Presentation Attacks Databases
9.5 Integration with Face Recognition Systems
9.6 Conclusion and Look Ahead on Face PAD
References
10 Recent Progress on Face Presentation Attack Detection of 3D Mask Attack
10.1 Background and Motivations
10.2 Publicly Available Datasets and Experiments Evaluation Protocol
10.2.1 Datasets
10.2.2 Evaluation Protocols
10.3 Methods
10.3.1 Appearance-Based Approach
10.3.2 Motion-Based Approach
10.3.3 Remote-Photoplethysmography-Based Approach
10.4 Experiments
10.4.1 Intra-Dataset Evaluation
10.4.2 Cross-Dataset Evaluation
10.5 Discussion and Open Challenges
References
11 Robust Face Presentation Attack Detection with Multi-channel Neural Networks
11.1 Introduction
11.2 Related Works
11.2.1 RGB Only Approaches (Feature Based and CNNs)
11.2.2 Multi-channel Methods
11.2.3 Open Challenges in PAD
11.3 PAD Approach
11.3.1 Preprocessing
11.3.2 Network Architectures for Multi-channel PAD
11.4 Experiments
11.4.1 Dataset: HQ-WMCA
11.4.2 Protocols
11.4.3 Metricsx
11.4.4 Implementation Details
11.4.5 Baselines
11.4.6 Experiments and Results
11.4.7 Computational Complexity
11.4.8 Discussions
11.5 Conclusions
References
12 Review of Face Presentation Attack Detection Competitions
12.1 Introduction
12.2 Review of Recent Face PAD Competitions
12.2.1 Multi-modal Face Anti-spoofing Attack Detection Challenge (CVPR2019)
12.2.2 Cross-Ethnicity Face Anti-spoofing Recognition Challenge (CVPR2020)
12.2.3 CelebA-Spoof Challenge on Face Anti-spoofing (ECCV2020)
12.2.4 LivDet-Face 2021—Face Liveness Detection Competition (IJCB2021)
12.2.5 3D High-Fidelity Mask Face Presentation Attack Detection Challenge (ICCV2021)
12.3 Discussion
12.3.1 General Observations
12.3.2 Lessons Learnt
12.3.3 Summary on Model Architectures
12.3.4 Future Challenges
12.4 Conclusions
References
Part IV Voice Biometrics
13 Introduction to Voice Presentation Attack Detection and Recent Advances
13.1 Introduction
13.2 Basics of ASV Spoofing and Countermeasures
13.2.1 Impersonation
13.2.2 Replay
13.2.3 Speech Synthesis
13.2.4 Voice Conversion
13.3 Summary of the Spoofing Challenges
13.3.1 ASVspoof 2015
13.3.2 ASVspoof 2017
13.3.3 ASVspoof 2019
13.4 Advances in Front-End Features
13.4.1 Front Ends for Detection of Voice Conversion and Speech Synthesis Spoofing
13.4.2 Front Ends for Replay Attack Detection
13.5 Advances in Back-End Classifiers
13.5.1 Generative Approaches
13.5.2 Discriminative Approaches
13.6 Other PAD Approaches
13.7 Future Directions of Anti-spoofing Research
13.8 Conclusion
References
14 A One-class Model for Voice Replay Attack Detection
14.1 Introduction
14.2 PRAD: Dataset for Replay Analysis
14.3 Distribution Analysis
14.3.1 Data Preparation
14.3.2 Analysis on Overall Distributions
14.3.3 Analysis on Important Factors
14.3.4 Analysis on Discrimination and Generalization
14.4 Dataset Analysis
14.4.1 ASVspoof 2019 Physical Access Dataset
14.4.2 ASVspoof 2017 Dataset
14.4.3 Cross Dataset Results
14.5 One-Class Model
14.5.1 Advocate One-Class Model
14.5.2 Model Design
14.5.3 Related Work
14.5.4 Experiments
14.6 Conclusions
References
15 Generalizing Voice Presentation Attack Detection to Unseen Synthetic Attacks and Channel Variation
15.1 Introduction
15.2 Generalize to Unseen Synthetic Attacks
15.2.1 One-Class Learning
15.2.2 Experiments
15.2.3 Discussions
15.3 Generalize to Channel Variation
15.3.1 Channel-Robust Strategies
15.3.2 Experiments
15.3.3 Discussions
15.4 Conclusions and Future Directions
15.5 Appendix
References
Part V Other Biometrics and Multi-Biometrics
16 Introduction to Presentation Attacks in Signature Biometrics and Recent Advances
16.1 Introduction
16.2 Review of PAD in Signature Biometrics
16.3 Presentation Attacks in Signature Biometrics
16.3.1 Types of Presentation Attacks
16.3.2 Synthetic Forgeries
16.4 On-Line Signature Databases
16.4.1 DeepSignDB
16.4.2 SVC2021_EvalDB
16.5 Experimental Work
16.5.1 On-line Signature Verification System
16.5.2 Experimental Protocol
16.5.3 Experimental Results
16.6 Conclusions
References
17 Extensive Threat Analysis of Vein Attack Databases and Attack Detection by Fusion of Comparison Scores
17.1 Introduction
17.2 Attack Databases
17.3 Threat Analysis
17.3.1 Threat Evaluation Protocol
17.3.2 Experimental Results
17.4 Attack Detection Using Score Level Fusion
17.4.1 Experimental Results
17.5 Summary
References
18 Fisher Vectors for Biometric Presentation Attack Detection
18.1 Introduction
18.2 Related Work
18.2.1 Hardware-Based Approaches
18.2.2 Software-Based PAD Approaches
18.3 Generalisable FV-Based PAD Approach
18.3.1 Reliable Features for Different Biometric Characteristics
18.3.2 Fisher Vector Encoding
18.3.3 Classification
18.4 Experimental Set-up
18.4.1 Databases
18.4.2 Evaluation Metrics
18.5 Experimental Results
18.5.1 Detection of Known PAI Species
18.5.2 Detection of Unknown PAI Species
18.5.3 Cross-Database
18.5.4 Common Feature Space Visualisation
18.6 Conclusion
References
19 Smartphone Multi-modal Biometric Presentation Attack Detection
19.1 Introduction
19.2 Related Work
19.2.1 Features of the SWAN Multi-modal Biometric Dataset
19.3 SWAN Multi-modal Biometric Dataset
19.3.1 Database Acquisition
19.3.2 SWAN Multi-modal Biometric Dataset
19.3.3 SWAN-Presentation Attack Dataset
19.3.4 Dataset Distribution
19.4 Experimental Performance Evaluation Protocols
19.5 Baseline Algorithms
19.5.1 Biometric Verification
19.5.2 Presentation Attack Detection Algorithms
19.6 Experimental Results
19.6.1 Biometric Verification Results
19.6.2 Biometric Vulnerability Assessment
19.6.3 Biometric PAD Results
19.7 Conclusions
References
Part VI Legal Aspects and Standards
20 Legal Aspects of Image Morphing and Manipulation Detection Technology
20.1 Introduction
20.2 Should Image Morphing and Image Manipulation Attack Detection (MAD) have a Legal Definition?
20.3 Privacy and Data Protection Aspects of MAD
20.3.1 The `Rule of Law' and Legality
20.3.2 Fairness
20.3.3 Transparency
20.3.4 Purpose Limitation and Legitimate Processing
20.3.5 Human Oversight and Intervention
20.4 Conclusion
21 Standards for Biometric Presentation Attack Detection
21.1 Introduction
21.2 International Standards Developed in ISO/IEC JTC
21.3 Development of Presentation Attack Detection Standard ISO/IEC 30107
21.4 Taxonomy for Presentation Attack Detection
21.5 Data Formats
21.6 Testing and Reporting
21.7 Conclusion and Future Work
References
Appendix Glossary
Index
