Transformer Architectures in Biometrics

Advanced applications of transformer architectures in biometric recognition systems, covering fingerprint, face, iris, voice, ECG, and multimodal biometrics with hands-on implementation.

📋 Course Overview

This repository contains materials for a graduate-level course exploring how self-attention mechanisms revolutionize biometric feature extraction, representation, and matching. Each week combines theoretical foundations with practical implementations using real datasets like SOCOFing, CelebA, ASVspoof, and PhysioNet ECG-ID.

📖 Full Course Details: See Syllabus for complete information including assessment, grading, and schedule.

🚀 Quick Start

Week 1: Foundational Transformer Architectures

Topics: Transformer fundamentals, self-attention mechanisms, attention visualization

• Evolution from RNNs/CNNs to transformers
• Self-attention mechanism: Query, Key, Value concepts
• Vision Transformers (ViT) for biometric images
• Lab: Visualizing attention patterns across biometric modalities (face, fingerprint, iris)

Week 2: Fingerprint Feature Extraction and Matching

Topics: Hybrid CNN-transformer architectures, quality-aware processing, SOCOFing dataset

• Advanced preprocessing: Gabor filtering, orientation estimation
• Core detection using Poincaré index
• Quality-aware attention mechanisms
• Lab: Complete fingerprint transformer with core-focused attention analysis

Week 3: Self-Attention for Minutiae Detection

Topics: Real-world minutiae detection challenges, attention-based detection, privacy-preserving biometrics

• Debugging "0 detection" problem with adaptive binarization
• Type-specific attention heads for ridge endings and bifurcations
• Cancelable biometric templates for privacy
• Lab: Three-notebook journey from problem discovery to production system

Week 4: Vision Transformers (ViT) for Facial Recognition

Topics: ViT architecture adaptation for face biometrics

• Face image patching strategies (16×16 patches)
• Face-specific position encoding
• Comparison with FaceNet and ArcFace
• Lab: Complete ViT implementation for face verification and identification

Week 5: Cross-Attention Networks for Facial Attribute Analysis

Topics: Multi-attribute learning with extreme class imbalance

• Cross-attention mechanisms for attribute-image relationships
• Handling 2% positive rate attributes (e.g., Bald, Mustache)
• Focal loss and aggressive weighting strategies
• Lab: Journey from all-negative predictions to successful multi-attribute classification

Week 6: Contactless Biometric Fusion

Topics: Quality assessment and fusion for contactless fingerprint/palmprint

• Traditional quality assessment (LQA_S and GQA_L)
• Two-stage fusion strategy with quality weighting
• Transformer-based quality assessment
• Lab: Complete fusion system with synthetic data generation using StyleGAN2

Week 7: Gait Recognition with Spatial-Temporal Transformers

🚧 Under Development - Coming Soon

Topics: Analyzing human gait patterns for identification at a distance

• Spatial-temporal transformer architectures
• Gait cycle analysis and feature extraction
• Cross-view gait recognition

Week 8: Audio Transformers for Speaker Verification with Anti-Spoofing

Topics: Dual-task transformers for speaker verification and liveness detection

• ASVspoof 2021 dataset integration
• Multi-objective training: speaker discrimination + anti-spoofing
• Attention to temporal inconsistencies in synthetic speech
• Lab: Production speaker verification system with integrated spoofing detection

Week 9: ECG Transformers for Biometric Authentication

Topics: Physiological biometrics using cardiac signals

• PhysioNet ECG-ID dataset processing
• Heartbeat segmentation and sequence creation
• Transformer architecture for ECG patterns
• Lab: Complete ECG authentication system with real-time processing capabilities

Week 10: Multimodal Biometric Fusion with Cross-Attention

🚧 Under Development - Coming Soon

Topics: Combining multiple biometric modalities

• Cross-attention for multimodal fusion
• Score-level and feature-level fusion strategies
• Handling missing modalities

📚 Course Resources

Required Software

• Python 3.8+
• PyTorch 2.0+
• CUDA 11.0+ (for GPU acceleration)
• See individual week READMEs for specific dependencies

Datasets Used

• SOCOFing: African fingerprint dataset (Weeks 2-3)
• CelebA: Facial attributes dataset (Week 5)
• LFW: Labeled Faces in the Wild (Week 4)
• ASVspoof 2021: Voice anti-spoofing (Week 8)
• PhysioNet ECG-ID: ECG biometrics (Week 9)

Reference Materials

• Biometric Transformer Cheatsheet
• Course Bibliography (if available)
• Individual chapter notes in each week's folder

🎯 Learning Outcomes

By completing this course, students will be able to:

• ✅ Implement transformer architectures for various biometric modalities
• ✅ Debug and optimize real-world biometric systems
• ✅ Handle extreme dataset imbalances and quality variations
• ✅ Build production-ready authentication systems
• ✅ Visualize and interpret attention mechanisms
• ✅ Apply privacy-preserving techniques to biometric data

📝 Assessment

• Weekly Labs: 40% - Hands-on implementation notebooks
• Assignments: 30% - Extended implementations and analysis reports
• Final Project: 30% - Research project on transformer-based biometrics

🤝 Contributing

This course is actively maintained. To report issues or suggest improvements:

• Submit GitHub issues for technical problems
• Use GitHub Discussions for course questions
• Pull requests welcome for bug fixes

📄 License

This course material is licensed under the MIT License. See LICENSE file for details.

🙏 Acknowledgments

Special thanks to:

• Dataset providers (SOCOFing, CelebA, ASVspoof, PhysioNet)
• Open-source contributors to PyTorch, Transformers, and biometric libraries
• Course contributors and reviewers

---

Course Repository: https://github.com/clarkson-edge/ee622