# Face Recognition Dataset

<!--
Project Information:
- Project ID: 22
- Title: Face Recognition Dataset
- Category: Image Data
- Description: Facial recognition dataset with labeled face images across multiple identities for face recognition and verification systems.
- Full Description: This dataset contains labeled face images with multiple images per identity, various poses, lighting conditions, and expressions. Perfect for face recognition, face verification, and biometric authentication systems.
- Technologies: PNG, JPG, NumPy, OpenCV, Face Recognition
- Difficulty: Intermediate
- Source Link: ./face-recognition/face-recognition.zip
- Demo Link: ./face-recognition/

Contact Information:
RSK World
Founder: Molla Samser
Designer & Tester: Rima Khatun
Email: help@rskworld.in
Phone: +91 93305 39277
Address: Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147
Website: https://rskworld.in/
Year: 2026
-->

## Overview

This dataset contains labeled face images with multiple images per identity, various poses, lighting conditions, and expressions. Perfect for face recognition, face verification, and biometric authentication systems.

## Features

### Core Features
- Multiple identities
- Various poses
- Different lighting conditions
- Facial landmarks
- Ready for face recognition models

### Advanced Features
- **Face Verification**: 1:1 face matching with confidence scores
- **Face Clustering**: Automatic grouping of similar faces
- **Quality Assessment**: Image quality scoring (blur, brightness, size)
- **Face Alignment**: Automatic face alignment for better accuracy
- **Data Augmentation**: Rotate, flip, brightness, contrast adjustments
- **Batch Processing**: Process multiple images efficiently
- **REST API**: Web API for face recognition services
- **Real-time Recognition**: Webcam-based face recognition

## Technologies Used

- **PNG/JPG**: Image formats
- **NumPy**: Numerical computing
- **OpenCV**: Computer vision library
- **Face Recognition**: Face recognition library

## Dataset Structure

```
face-recognition/
├── data/
│ ├── train/
│ │ ├── person_001/
│ │ ├── person_002/
│ │ └── ...
│ ├── test/
│ │ ├── person_001/
│ │ ├── person_002/
│ │ └── ...
│ └── validation/
├── models/
├── scripts/
│ ├── load_dataset.py
│ ├── preprocess.py
│ ├── train_model.py
│ └── recognize_faces.py
├── requirements.txt
├── config.py
└── README.md
```

## Installation

1. Clone or download this repository
2. Install required dependencies:

```bash
pip install -r requirements.txt
```

## Quick Start

### 1. Create Sample Data

```bash
python create_sample_data.py
```

### 2. Train the Model

```bash
python train_model.py
```

### 3. Run Tests

```bash
python test_system.py
```

### 4. Try Examples

```bash
python example_usage.py
python advanced_demo.py
python demo.py
```

## Usage

### Basic Face Recognition

```python
from scripts.recognize_faces import FaceRecognizer

recognizer = FaceRecognizer()
recognizer.train('data/train', save_model=True)
results = recognizer.recognize('path/to/image.jpg')
```

### Face Verification (1:1 Matching)

```python
from scripts.advanced_features import FaceVerifier

verifier = FaceVerifier()
result = verifier.verify_images('image1.jpg', 'image2.jpg')
print(f"Match: {result['is_match']}, Confidence: {result['confidence']:.2%}")
```

### Face Clustering

```python
from scripts.advanced_features import FaceClustering
from scripts.preprocess import get_face_encoding

clusterer = FaceClustering()
encodings = [get_face_encoding(img) for img in images]
result = clusterer.cluster(encodings)
```

### Quality Assessment

```python
from scripts.advanced_features import FaceQualityAssessment

assessor = FaceQualityAssessment()
quality = assessor.assess(image, face_location)
print(f"Overall quality: {quality['overall_score']:.2f}")
```

### Data Augmentation

```python
from scripts.data_augmentation import FaceAugmenter

augmenter = FaceAugmenter()
augmented = augmenter.augment_image(image, ['rotate', 'flip', 'brightness'])
```

### REST API Server

```bash
python scripts/api_server.py
```

Then use the API endpoints:
- `POST /api/recognize` - Recognize faces
- `POST /api/verify` - Verify two faces
- `POST /api/quality` - Assess image quality
- `POST /api/cluster` - Cluster face encodings

## Requirements

See `requirements.txt` for the complete list of dependencies.

## License

This dataset is provided for educational and research purposes.

## Contact

**RSK World**
- Founder: Molla Samser
- Designer & Tester: Rima Khatun
- Email: help@rskworld.in
- Phone: +91 93305 39277
- Address: Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147
- Website: https://rskworld.in/

© 2026 RSK World. All rights reserved.

# Release Notes - Face Recognition Dataset v1.0.0

**Release Date:** 2026
**Version:** 1.0.0
**Repository:** https://github.com/rskworld/face-recognition

---

## 🎉 Initial Release

This is the initial release of the Face Recognition Dataset project - a complete, production-ready face recognition system with advanced features, comprehensive documentation, and REST API support.

## ✨ Features

### Core Features
- ✅ **Face Recognition**: Train and recognize faces from images
- ✅ **Multiple Identities**: Support for multiple labeled identities
- ✅ **Real-time Recognition**: Webcam-based live face recognition
- ✅ **Model Training**: Automatic face encoding extraction and model training
- ✅ **Dataset Management**: Organized dataset loading and preprocessing

### Advanced Features
- ✅ **Face Verification**: 1:1 face matching with confidence scores
- ✅ **Face Clustering**: Automatic grouping of similar faces using DBSCAN
- ✅ **Quality Assessment**: Image quality scoring (blur, brightness, size)
- ✅ **Face Alignment**: Automatic face alignment for better accuracy
- ✅ **Data Augmentation**: 8+ augmentation techniques (rotate, flip, brightness, contrast, noise, blur, crop)
- ✅ **Batch Processing**: Process multiple images efficiently
- ✅ **REST API**: Complete Flask-based REST API with 5 endpoints

### Documentation
- ✅ **Complete README**: Comprehensive project documentation
- ✅ **Quick Start Guide**: Step-by-step installation and usage
- ✅ **Installation Guide**: Detailed setup instructions
- ✅ **Features Documentation**: Complete feature list with examples
- ✅ **HTML Demo Page**: Interactive web-based documentation
- ✅ **API Documentation**: REST API usage examples

### Sample Data
- ✅ **25 Sample Images**: Pre-generated test data (5 identities × 5 images)
- ✅ **Working Examples**: Ready-to-run demo scripts
- ✅ **Test Suite**: Comprehensive testing framework

## 📦 What's Included

### Python Scripts
- `config.py` - Configuration settings
- `train_model.py` - Model training script
- `create_sample_data.py` - Sample data generator
- `test_system.py` - Comprehensive test suite
- `example_usage.py` - Usage examples
- `advanced_demo.py` - Advanced features demo
- `demo.py` - Interactive demo (webcam/image)
- `setup_dataset.py` - Dataset setup utility
- `check_errors.py` - Error checking utility

### Scripts Package
- `scripts/load_dataset.py` - Dataset loading utilities
- `scripts/preprocess.py` - Image preprocessing
- `scripts/recognize_faces.py` - Face recognition system
- `scripts/advanced_features.py` - Advanced features (verification, clustering, quality, alignment, batch)
- `scripts/data_augmentation.py` - Data augmentation utilities
- `scripts/api_server.py` - REST API server
- `scripts/visualize.py` - Visualization utilities

### Documentation
- `README.md` - Main project documentation
- `QUICKSTART.md` - Quick start guide
- `INSTALLATION_GUIDE.md` - Detailed installation instructions
- `FEATURES.md` - Complete feature documentation
- `PROJECT_SUMMARY.md` - Project overview
- `INDEX.md` - Complete file index
- `ISSUES_FIXED.md` - Issues and fixes documentation
- `index.html` - Interactive HTML documentation page

### Configuration
- `requirements.txt` - Python dependencies
- `.gitignore` - Git ignore rules
- `LICENSE` - MIT License
- `project_metadata.json` - Project metadata

## 🚀 Quick Start

```bash
# 1. Install dependencies
pip install -r requirements.txt

# 2. Set up directory structure
python setup_dataset.py

# 3. Create sample data
python create_sample_data.py

# 4. Train the model
python train_model.py

# 5. Test the system
python test_system.py

# 6. Run demo
python demo.py
```

## 📊 Project Statistics

- **Total Files:** 25+ Python scripts
- **Sample Images:** 25 (5 identities × 5 images)
- **Features:** 10+ advanced features
- **API Endpoints:** 5 REST endpoints
- **Test Coverage:** 8 comprehensive tests
- **Documentation:** 8+ documentation files

## 🔧 Technologies

- Python 3.7+
- NumPy - Numerical computing
- OpenCV - Computer vision
- face-recognition - Face recognition library
- scikit-learn - Clustering algorithms
- Flask - REST API framework
- Matplotlib - Visualization

## 📝 API Endpoints

- `GET /api/health` - Health check
- `POST /api/recognize` - Recognize faces
- `POST /api/verify` - Verify two faces
- `POST /api/quality` - Assess image quality
- `POST /api/cluster` - Cluster face encodings

## 🐛 Known Issues

None - All issues have been resolved in this release.

## 🔄 Changelog

### v1.0.0 (2026)
- Initial release
- Complete face recognition system
- Advanced features implementation
- Comprehensive documentation
- Sample data generation
- REST API server
- Test suite
- Error checking utilities

## 📞 Support

**RSK World**
- **Founder:** Molla Samser
- **Designer & Tester:** Rima Khatun
- **Email:** help@rskworld.in
- **Phone:** +91 93305 39277
- **Address:** Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147
- **Website:** https://rskworld.in/

## 📄 License

MIT License - See LICENSE file for details.

## 🙏 Acknowledgments

- Built with educational and research purposes in mind
- Uses publicly available libraries and tools
- Sample data generated for testing purposes

---

**© 2026 RSK World. All rights reserved.**

Repository: https://github.com/rskworld/face-recognition