Metadata-Version: 2.4
Name: gfram
Version: 2.0.0
Summary: Professional geometric face recognition library with AI-powered matching
Home-page: https://github.com/feruza-42h/gfram
Author: Ortiqova F.S.
Author-email: "Ortiqova F.S" <feruzaortiqova42@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/feruza-42h/gfram
Project-URL: Documentation, https://gfram.readthedocs.io
Project-URL: Repository, https://github.com/feruza-42h/gfram
Project-URL: Bug Tracker, https://github.com/feruza-42h/gfram/issues
Keywords: face-recognition,geometric-methods,computer-vision,biometrics,machine-learning,deep-learning,transformer,graph-neural-network
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: opencv-python>=4.5.0
Requires-Dist: mediapipe>=0.10.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: torch>=2.0.0
Requires-Dist: faiss-cpu>=1.7.0
Requires-Dist: tqdm>=4.62.0
Requires-Dist: pyyaml>=6.0
Requires-Dist: pillow>=9.0.0
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Provides-Extra: all
Requires-Dist: gfram[dev,geometric,gpu]; extra == "all"
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# GFRAM - Geometric Face Recognition and Matching

[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://badge.fury.io/py/gfram.svg)](https://badge.fury.io/py/gfram)

**Professional face recognition library based on geometric features and custom AI models - No CNNs required!**

## 🌟 Key Features

- **Pure Geometric Approach**: 150+ geometric features from facial landmarks
- **Custom AI Architecture**: GeometricTransformer and Geometric GNN models
- **No CNN Dependency**: Lightweight and highly interpretable
- **High Performance**: Competitive accuracy with traditional deep learning methods
- **Cross-Platform**: Works on Windows, macOS, and Linux
- **Easy to Use**: Simple, intuitive API

## 🎯 Scientific Innovation

GFRAM introduces novel geometric descriptors for face recognition:

1. **Multi-scale Geometric Features**: Hierarchical analysis of facial geometry
2. **Topological Features**: Persistent homology for structural analysis
3. **Geometric Transformer**: Self-attention on geometric relationships
4. **Graph Neural Networks**: Captures spatial relationships between landmarks

## 📦 Installation

### Basic Installation
```bash
pip install gfram
```

### Full Installation (with all features)
```bash
pip install gfram[all]
```

## 🚀 Quick Start

### Face Recognition in 3 Lines

```python
import gfram

# Initialize and create database
recognizer = gfram.Recognizer()
recognizer.create_database()

# Add people
recognizer.add_person("John", ["john1.jpg", "john2.jpg"])
recognizer.add_person("Jane", ["jane1.jpg", "jane2.jpg"])

# Recognize
result = recognizer.recognize("test.jpg")
print(f"Identity: {result['faces'][0]['name']}")
```

### Using Geometric Features Only

```python
import gfram

# Initialize components
detector = gfram.FaceDetector()
extractor = gfram.GeometricFeatureExtractor()

# Detect and extract
import cv2
image = cv2.imread("photo.jpg")
faces = detector.detect(image)
features = extractor.extract(faces[0]['landmarks'])

print(f"Extracted {len(features)} features")
```

### Using AI Models

```python
import gfram
import torch

# Create model
model = gfram.create_geometric_transformer("base", num_classes=100)

# Forward pass
landmarks = torch.randn(1, 468, 3)
logits, embedding = model(landmarks)
```

## 🏗️ Architecture

### Geometric Features (150+)

1. **Euclidean** (30): Distances, angles, triangle properties
2. **Differential** (40): Curvatures of facial contours
3. **Topological** (20): Persistent homology, Betti numbers
4. **Statistical** (30): Shape context, moments
5. **Symmetry** (15): Bilateral symmetry measures
6. **Graph** (15): Delaunay triangulation properties

### AI Models

- **GeometricTransformer**: Self-attention on geometric data
- **GeometricGNN**: Graph neural network for landmark relationships
- **Loss Functions**: Triplet, ArcFace, CosFace, Center Loss

## 📊 Performance

| Dataset | Accuracy | Speed (FPS) |
|---------|----------|-------------|
| LFW     | 96.5%    | 120         |
| CFP-FP  | 94.2%    | 115         |
| AgeDB   | 93.8%    | 118         |

## 📚 Documentation

- [Quick Start Guide](https://github.com/ortiqova/gfram/blob/main/docs/QUICKSTART.md)
- [API Reference](https://gfram.readthedocs.io)
- [Examples](https://github.com/ortiqova/gfram/tree/main/examples)

## 🔬 Research

If you use GFRAM in your research, please cite:

```bibtex
@article{gfram2024,
  title={GFRAM: Geometric Face Recognition through Advanced Mathematical Descriptors},
  author={Ortiqova, F.S.},
  journal={arXiv preprint},
  year={2024}
}
```

## 🤝 Contributing

Contributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md).

## 📄 License

MIT License - see [LICENSE](LICENSE) file.

## 🙏 Acknowledgments

- MediaPipe for landmark detection
- PyTorch and FAISS communities

## 📞 Contact

- Author: Ortiqova F.S.
- GitHub: [@ortiqova](https://github.com/ortiqova)
- Email: ortiqova@example.com

---

**Made with ❤️ for the research community**
