Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/rabbicse/face-app
A Face recognition system using TensorFlow.js for in-browser image processing and backend services powered by PyTorch, Qdrant, RetinaFace, ArcFace, and OpenCV for detection, embedding, and similarity matching. This setup ensures efficient, secure, and accurate facial recognition.
https://github.com/rabbicse/face-app
arcface deep-neural-networks docker docker-compose face-detection face-recognition machine-learning nextjs numpy opencv python pytorch react retinaface tensorflowjs typescript typescript-react
Last synced: about 16 hours ago
JSON representation
A Face recognition system using TensorFlow.js for in-browser image processing and backend services powered by PyTorch, Qdrant, RetinaFace, ArcFace, and OpenCV for detection, embedding, and similarity matching. This setup ensures efficient, secure, and accurate facial recognition.
- Host: GitHub
- URL: https://github.com/rabbicse/face-app
- Owner: rabbicse
- License: gpl-3.0
- Created: 2021-07-30T05:43:02.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2025-01-07T17:49:39.000Z (3 days ago)
- Last Synced: 2025-01-07T18:49:00.427Z (3 days ago)
- Topics: arcface, deep-neural-networks, docker, docker-compose, face-detection, face-recognition, machine-learning, nextjs, numpy, opencv, python, pytorch, react, retinaface, tensorflowjs, typescript, typescript-react
- Language: TypeScript
- Homepage:
- Size: 36.9 MB
- Stars: 1
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Face Recognition Application
[](https://hub.docker.com/r/meheditech/frs-backend)
## Overview
**Face Recognition Application** is a cutting-edge facial recognition application built with modern technologies. It supports face detection, keypoint extraction and embedding generation. The app is optimized for high performance and accuracy, designed for use in identity verification, security and other facial recognition applications.
## Features
- **Face Detection**: Detect faces in real-time or static images.
- **Facial Landmarks Extraction**: Identify key facial points for advanced facial analysis.
- **Embedding Generation**: Generate facial embeddings for comparison and recognition.
- **API Support**: Provides RESTful APIs for integration.
## Technologies Used
- **Frontend**: React.js, Nextjs
- **Backend**: Python (FastAPI)
- **Face Detection - Backend**: PyTorch, RetinaFace
- **Face Recognition - Backend**: PyTorch, ArcFace
- **Face Detection - Frontend**: Tensorflowjs, Mediapipe
- **Database**: Qdrant (Vector Database), PostgreSQL (Optional for storage)
- **Containerization**: Docker
## Installation
### Prerequisites
Ensure you have the following installed:
- Node.js (v22 or higher)
- Python (v3.12 or higher)
- Docker (Optional, for containerized setup)
- CUDA Toolkit (Optional, for GPU acceleration)
### Steps
1. Clone the repository:
```bash
git clone https://github.com/rabbicse/face-app.git
cd face-app
```
2. Install dependencies for the frontend:
```bash
cd frontend/frs_react
npm install
```
3. Set up the backend:
```bash
cd backend/frs
pip install -r requirements.txt
```
4. Configure environment variables:
- Create a `.env` file in the root directory.
- Add the following variables:
```env
NEXT_PUBLIC_API_HOST=http://localhost
NEXT_PUBLIC_API_PORT=5000
```
5. Start the services:
- Frontend:
```bash
cd frontend/frs_react
npm run dev
```
- Backend:
```bash
cd backend/frs
uvicorn api.server:app --reload --host 0.0.0.0 --port 5000
```
6. Access the application:
Open your browser and navigate to `http://localhost:3000`.
## Screenshots
### Login Page
### Login with Face Recognition
## API Endpoints
### 1. Detect Faces
**Endpoint:** `/api/v1/dnn/detect`
- **Method:** `POST`
- **Body:** `multipart/form-data`
- `photo`: The image file for detection.
- **Response:**
```json
{
"status": "success",
"faces": [
{
"bbox": {
"x_min": 0.2697347104549408,
"y_min": 0.1822991967201233,
"x_max": 0.7142632007598877,
"y_max": 0.7912323474884033,
"score": 0.9963470101356506
},
"landmarks": {
"left_eye": {
"x": 0.3953465223312378,
"y": 0.413769394159317
},
"right_eye": {
"x": 0.5912481546401978,
"y": 0.4187081754207611
},
"nose": {
"x": 0.49022188782691956,
"y": 0.5452162623405457
},
"left_lip": {
"x": 0.4161381721496582,
"y": 0.6275689005851746
},
"right_lip": {
"x": 0.5787203907966614,
"y": 0.6303802132606506
}
}
}
]
}
```
### 2. Extract Embeddings
**Endpoint:** `/api/v1/dnn/extract-embedding`
- **Method:** `POST`
- **Body:** `multipart/form-data`
- `photo`: The image file for embedding extraction.
- **Response:**
```json
{
"status": 0,
"embedding": "MQoaPfbAZ73Kape9Rpw...fhs="
}
```
## Docker Setup
To run the application in Docker, follow these steps:
1. Build and start the containers:
```bash
docker-compose up --build
```
2. Access the application at `http://localhost:3000`.
## Contribution
Contributions are welcome! Please follow these steps:
1. Fork the repository.
2. Create a new branch: `git checkout -b feature/your-feature-name`.
3. Commit your changes: `git commit -m 'Add some feature'`.
4. Push to the branch: `git push origin feature/your-feature-name`.
5. Open a pull request.
## License
This project is licensed under the GNU GENERAL PUBLIC License. See the [LICENSE](LICENSE) file for details.
## References
- [InsightFace](https://github.com/deepinsight/insightface)
- [tfjs-models](https://github.com/tensorflow/tfjs-models)
---
Made with ❤️ by [Mehedi Hasan](https://github.com/rabbicse)