🔍 Face Recognition for Attendance Systems using Haar Cascade Algorithm

This repository contains a face recognition project designed to automate attendance management systems using computer vision techniques.

🔍 Overview

This project is a Python-based implementation of face recognition using the Haar Cascade algorithm. It is currently in its initial stages and serves as a foundation for building a more advanced face recognition system. The project comprises the following key components:

📸 generateimages.py: A script to capture and save images of known individuals for training the recognition model.
📦 createdataandlabel.py: A script to preprocess the captured images, create labels, and organize the training dataset.
🤖 model.py: Contains the code for training a face recognition model using the preprocessed data.
🧪 testing.py: A script to test the trained model on new images and perform face recognition.

🚀 Getting Started

These instructions will guide you through setting up the project on your local machine for development and testing.

Prerequisites

Before you start, make sure you have the following prerequisites:

🐍 Python 3.x
Required Python libraries: OpenCV, Numpy, TensorFlow, tqdm

💻 Installation

Follow these steps to install the required libraries for the project:

Clone the repository to your local machine.

git clone https://github.com/your-username/repo-name.git
cd repo-name

Install the necessary libraries using pip:

🖼 OpenCV: For image processing.
```
pip install opencv-python
```
🔢 NumPy: For numerical operations and array handling.
```
pip install numpy
```
🤖 TensorFlow: For machine learning and deep learning tasks.
```
pip install tensorflow
```
📊 tqdm: For displaying progress bars during time-consuming operations.
```
pip install tqdm
```

📚 Usage

Here's how to use the project for face recognition:

Capture images of known individuals using generateimages.py. Organize these images into folders with each person's name.
```
python generateimages.py
```
Run createdataandlabel.py to preprocess the captured images, create training data, and store them in the data folder.
```
python createdataandlabel.py
```
Train the face recognition model using model.py.
```
python model.py
```
Test the trained model on new images for face recognition using testing.py.
```
python testing.py
```

🤝 Contributing

We welcome contributions from the community. If you want to contribute to this project, please follow our contribution guidelines.

📄 License

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


Feel free to use or modify this README content with emojis as needed for your project.

Charishma608/Face-Recognition-for-Attendance-Systems