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FACE RECOGNITION DATASET PROJECT
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Project ID: 22
Title: Face Recognition Dataset
Category: Image Data
Difficulty: Intermediate

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 (Image formats)
- NumPy (Numerical computing)
- OpenCV (Computer vision)
- Face Recognition (Face recognition library)

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

Source Link: ./face-recognition/face-recognition.zip
Demo Link: ./face-recognition/
Project Image: ./face-recognition/face-recognition.png

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CONTACT INFORMATION
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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

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PROJECT STRUCTURE
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face-recognition/
├── data/
│   ├── train/          - Training images (person_name/image.jpg)
│   ├── test/           - Test images
│   └── validation/     - Validation images
├── models/             - Saved models
├── scripts/
│   ├── __init__.py
│   ├── load_dataset.py - Dataset loading utilities
│   ├── preprocess.py   - Image preprocessing
│   ├── recognize_faces.py - Face recognition
│   └── visualize.py    - Visualization utilities
├── config.py           - Configuration settings
├── train_model.py      - Main training script
├── example_usage.py    - Usage examples
├── demo.py             - Interactive demo
├── setup_dataset.py    - Dataset setup utility
├── requirements.txt    - Python dependencies
├── README.md           - Main documentation
├── QUICKSTART.md       - Quick start guide
├── index.html          - HTML demo page
├── LICENSE             - License information
└── project_metadata.json - Project metadata

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QUICK START
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1. Install dependencies:
   pip install -r requirements.txt

2. Set up directory structure:
   python setup_dataset.py

3. Organize your images:
   Place images in data/train/person_name/image.jpg format

4. Train the model:
   python train_model.py

5. Run examples:
   python example_usage.py

6. Interactive demo:
   python demo.py

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© 2026 RSK World. All rights reserved.
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