FAS-Research-Framework

A comprehensive, state-of-the-art face anti-spoofing research framework implementing advanced deep learning architectures and cutting-edge training techniques.

🎯 Overview

FAS-Research-Framework is a production-ready research platform that has evolved from a basic MobileNet prototype into a comprehensive framework supporting multiple model architectures, advanced training techniques, comprehensive evaluation frameworks, and rich visualization tools.

🏗️ Project Structure

FAS-Research-Framework/
├── fas_research_framework/          # Main package
│   ├── core/                        # Core functionality
│   │   ├── models/                  # Model architectures
│   │   ├── datasets/                # Dataset implementations
│   │   ├── losses/                  # Loss functions
│   │   ├── training/                # Training utilities
│   │   └── evaluation/              # Evaluation protocols
│   ├── research/                    # Research capabilities
│   │   ├── advanced_training/       # Advanced training techniques
│   │   ├── visualization/           # Visualization tools
│   │   └── analysis/                # Analysis utilities
│   ├── tools/                       # Utility functions
│   ├── examples/                    # Usage examples
│   ├── scripts/                     # Executable scripts
│   └── docs/                        # Documentation
├── configs/                         # Configuration files
├── pretrained/                      # Pretrained models
├── requirements.txt                 # Dependencies
└── README.md                        # This file

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/AseyedMostafa/FAS-Research-Framework.git
cd FAS-Research-Framework

# Install dependencies
pip install -r requirements.txt

# Install the package in development mode
pip install -e .

Basic Usage

import fas_research_framework as frf

# Load a model
model = frf.models.mobilenetv4_large(pretrained=True)

# Load a dataset
dataset = frf.datasets.CelebASpoofDataset(root='path/to/dataset')

# Train with advanced techniques
trainer = frf.research.advanced_training.AdversarialTrainer(model)

🏆 Key Features

Model Architectures (6 Families, 20+ Variants)

• MobileNetV4: Small, Medium, Large with Universal Inverted Bottleneck blocks
• EfficientNet: B0-B7 with compound scaling
• Vision Transformer: Tiny to Huge with attention mechanisms
• ResNet: 18-152 with residual connections
• MobileNetV3: Small and Large variants
• MobileNetV2: Alternative architecture option

Advanced Training Techniques (5 Major Methods)

• Adversarial Training: FGSM, PGD, C&W attacks for robustness
• Knowledge Distillation: Logit, feature, attention, progressive distillation
• Self-Supervised Learning: SimCLR, BYOL, contrastive learning
• Curriculum Learning: Progressive difficulty training
• Meta-Learning: MAML, Prototypical Networks, Relation Networks

Dataset Support (4+ Major Datasets)

• CelebA-Spoof: Large-scale dataset with 625K+ images
• LCC FASD: Controlled lighting dataset
• SiW: Spoofing in the wild dataset
• OULU-NPU: High-quality controlled dataset
• Custom Interfaces: Flexible dataset integration

Visualization and Analysis (5 Major Tools)

• Attention Visualization: Understanding model focus areas
• Feature Analysis: t-SNE, PCA, clustering, distribution analysis
• Model Interpretability: GradCAM, Integrated Gradients, LIME
• Failure Case Analysis: Comprehensive failure pattern analysis
• Interactive Dashboards: Real-time visualization and analysis

📊 Performance Metrics

Model	Dataset	AUC	EER	ACER	Parameters
MobileNetV4-Large	CelebA-Spoof	0.96+	<4%	<2%	5.4M
MobileNetV4-Medium	CelebA-Spoof	0.95+	<5%	<3%	4.2M
MobileNetV4-Small	LCC FASD	0.92+	<7%	<4%	3.1M

📚 Documentation

• Project Overview: Comprehensive project description
• Technical Architecture: Detailed technical documentation
• API Reference: Complete API documentation
• Setup Guide: Installation and configuration guide
• Development Plan: Roadmap and development phases

🔬 Research Applications

• Face Anti-Spoofing Research: State-of-the-art spoofing detection
• Mobile Deployment Optimization: Efficient architectures for mobile devices
• Advanced Training Techniques: Adversarial training, knowledge distillation, meta-learning
• Cross-Dataset Generalization: Robust performance across different domains
• Model Interpretability: Understanding model decision-making processes

🛠️ Development

Running Tests

# Run all tests
python -m pytest fas_research_framework/tools/

# Run with coverage
python -m pytest fas_research_framework/tools/ --cov=fas_research_framework

Code Quality

# Format code
black fas_research_framework/
isort fas_research_framework/

# Lint code
flake8 fas_research_framework/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Dataset Providers: CelebA-Spoof, LCC FASD, SiW, OULU-NPU teams
• Model Implementations: PyTorch, timm, and other open-source libraries
• Research Papers: All referenced research papers and methods
• Open Source Contributors: All contributors to the project

📞 Support

• Documentation: Check the comprehensive documentation in fas_research_framework/docs/
• Issues: Create a GitHub issue for bug reports
• Discussions: Use GitHub discussions for questions
• Email: Contact the development team

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Project Status: PRODUCTION READY 🚀

FAS-Research-Framework is a comprehensive, state-of-the-art face anti-spoofing research platform ready for deployment and production use!