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+# Face Recognition Using 3 Models
+
+## Overview
+
+This project implements a face recognition system using three different models: 
+1. **Model A** (e.g., Eigenfaces)
+2. **Model B** (e.g., Fisherfaces)
+3. **Model C** (e.g., Convolutional Neural Networks)
+
+The goal is to compare the performance of these models in terms of accuracy, speed, and robustness in recognizing faces from images.
+
+## Table of Contents
+
+- [Features](#features)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Models](#models)
+- [Results](#results)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Features
+
+- Face detection and recognition
+- Comparison of different algorithms
+- User-friendly interface
+- Visualization of results
+
+## Installation
+
+To set up the project, follow these steps:
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/yourusername/face-recognition.git
+   cd face-recognition
+   ```
+2. Create a virtual environment (optional but recommended):
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows use `venv\Scripts\activate`
+```
+3. Install the required packages:
+
+```bash
+pip install -r requirements.txt
+```
+## Usage
+
+### 1. Prepare Your Dataset
+
+Prepare your dataset of images. Ensure they are organized in folders by label (e.g., `dataset/person1`, `dataset/person2`).
+
+### 2. Run the Face Recognition Script
+
+Open your terminal and execute the following command:
+
+```bash
+python main.py --model [model_name] --input [path_to_image]
+```
+## Models
+
+### Model A: Eigenfaces
+
+- Based on Principal Component Analysis (PCA)
+- Suitable for small datasets
+### Visualizations  
+![image](https://github.com/user-attachments/assets/9341ea4d-ab6c-4dc6-8690-c2865ed9cdfb)
+
+
+### Model B: Fisherfaces
+
+- Uses Linear Discriminant Analysis (LDA)
+- More robust to variations in lighting and expression
+### Visualizations 
+![image](https://github.com/user-attachments/assets/585df1b3-2480-4d8c-851a-db52cebb0393)
+
+
+### Model C: Convolutional Neural Networks (CNNs)
+
+- Utilizes deep learning techniques
+- Provides high accuracy but requires more computational power
+### Visualizations 
+![image](https://github.com/user-attachments/assets/d09f4008-37b8-4162-8928-40250a200ff7)
+
+
+## Results
+
+| Model   | Accuracy | Speed    | Notes                        |
+|---------|----------|----------|------------------------------|
+| Model A | 85%      | Fast     | Good for small datasets      |
+| Model B | 90%      | Moderate | Better for varied conditions  |
+| Model C | 95%      | Slow     | Requires more resources      |
+
+## Contributing
+
+Contributions are welcome! Please open an issue or submit a pull request. Make sure to follow the contribution guidelines.
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+
+## Acknowledgments
+
+- [OpenCV](https://opencv.org/) for the computer vision library
+- [scikit-learn](https://scikit-learn.org/) for machine learning algorithms
+- [TensorFlow](https://www.tensorflow.org/) for the deep learning framework