https://github.com/abhijithganesh/rekognize
This project is an attendance system that uses face detection technology to track and record attendance. It is designed to provide a more efficient and accurate method of attendance tracking, reducing the need for manual input and minimizing errors.
https://github.com/abhijithganesh/rekognize
face face-recognition fastapi mtcnn onnx python pytorch
Last synced: 4 months ago
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This project is an attendance system that uses face detection technology to track and record attendance. It is designed to provide a more efficient and accurate method of attendance tracking, reducing the need for manual input and minimizing errors.
- Host: GitHub
- URL: https://github.com/abhijithganesh/rekognize
- Owner: AbhijithGanesh
- License: mit
- Created: 2024-05-29T15:47:36.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-06-10T12:22:01.000Z (over 1 year ago)
- Last Synced: 2025-02-28T19:09:26.187Z (7 months ago)
- Topics: face, face-recognition, fastapi, mtcnn, onnx, python, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 7.52 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Face Detection for Attendance
## Project Overview
This project is an attendance system that uses face detection technology to track and record attendance. It is designed to provide a more efficient and accurate method of attendance tracking, reducing the need for manual input and minimizing errors.
## Tools used
- PyTorch
- FastAPI
- [FaceNet Torch](https://github.com/timesler/facenet-pytorch/)
- MTCNN and VGGFace (Not really tools but deserve a mention)
## Features
- Face detection: The system uses advanced face detection algorithms to identify individuals.
- Attendance tracking: Once a face is detected and recognized, the system automatically marks the individual as present.
## Installation
1. Clone the repository: `git clone https://github.com/AbhijithGanesh/Rekognize.git`
2. Navigate to the project directory: `cd Rekognize`
3. Install the required dependencies: `pip install -r src/requirements.txt`
4. Run the application: `python main.py`
## Usage
To use the system, simply start the application and position the camera to capture the faces of the individuals. The system will automatically detect the faces and mark the attendance.
## Contributing
Contributions are welcome!
## License
This project is licensed under the terms of the MIT license. See the [LICENSE](LICENSE.md) file for details.
## Contributors
- [Abhijith Ganesh](https://github.com/AbhijithGanesh)
- [Allen Bijo T](https://github.com/AllenBijo)
## Disclaimer
This is a public fork of a private repository we worked on. Please re-train/change the dataset according to your use case.