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https://github.com/mayankghatawal/facerecognisation
Face Recognisation Attendance System Using Machine Learning
https://github.com/mayankghatawal/facerecognisation
artificial-intelligence backend face face-recognition firebase frontend machine-learning python streamlit
Last synced: about 16 hours ago
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Face Recognisation Attendance System Using Machine Learning
- Host: GitHub
- URL: https://github.com/mayankghatawal/facerecognisation
- Owner: MayankGhatawal
- Created: 2024-07-09T05:52:01.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-10-30T16:07:05.000Z (20 days ago)
- Last Synced: 2024-10-30T17:20:36.315Z (20 days ago)
- Topics: artificial-intelligence, backend, face, face-recognition, firebase, frontend, machine-learning, python, streamlit
- Language: Python
- Homepage: https://face-recognisation-nu.vercel.app
- Size: 292 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Face Recognition Attendance System
This project is a Face Recognition Attendance System built using Streamlit and OpenCV. The application captures images from a webcam, recognizes faces, and marks attendance in real-time.
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Dependencies](#dependencies)
- [Project Structure](#project-structure)
- [License](#license)
## Features
- Real-time face detection and recognition.
- Marking attendance with timestamp.
- User-friendly interface built with Streamlit.
- Storage of attendance records in CSV format.
- Easy to add new faces to the recognition system.
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/your-username/face-recognition-attendance-system.git
cd face-recognition-attendance-system
```
2. **Create a virtual environment:**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
4. **Download the pre-trained face detection model:**
Download the `haarcascade_frontalface_default.xml` file from [OpenCV GitHub](https://github.com/opencv/opencv/tree/master/data/haarcascades) and place it in the `models` directory.
## Usage
1. **Run the Streamlit app:**
```bash
streamlit run app.py
```
2. **Adding New Faces:**
- Use the "Add New Faces" section to capture and store new faces.
- Provide the name of the person and capture their face using the webcam.
3. **Marking Attendance:**
- Go to the "Mark Attendance" section.
- The app will use the webcam to recognize faces and mark attendance.
4. **Viewing Attendance Records:**
- Check the "Attendance Records" section to view the stored attendance data.
## Dependencies
- Python 3.x
- Streamlit
- OpenCV
- NumPy
- Pandas
## Project Structure
```
face-recognition-attendance-system/
├── app.py # Main Streamlit application
├── face_recognition.py # Face recognition logic
├── attendance.py # Attendance marking logic
├── data/ # Directory to store face images and attendance records
│ ├── faces/ # Subdirectory for storing face images
│ └── attendance.csv # CSV file for storing attendance records
├── models/ # Directory for storing machine learning models
│ └── haarcascade_frontalface_default.xml # Pre-trained face detection model
├── requirements.txt # List of dependencies
└── README.md # Project README file
```
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
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