This is a modern Face Authentication System which includes state-of-art algorithms to detect face and generate face embedding. This system contains endpoints which can be integrated to any device depending on the requirements.
Before we run the project, make sure that you are having MongoDB in your local system, with Compass since we are using MongoDB for data storage. You also need Azure account to access the service like ACS and App services.
git clone https://github.com/Deep-Learning-01/Deep-Authenticator-Frontend.git
conda create -p ./env python=3.8.13 -y
conda activate face_auth
pip install -r requirements.txt
export SECRET_KEY=<SECRET_KEY>
export ALGORITHM=<ALGORITHM>
export MONGODB_URL_KEY=<MONGODB_URL_KEY>
export DATABASE_NAME=<DATABASE_NAME>
export USER_COLLECTION_NAME=<USER_COLLECTION_NAME>
export EMBEDDING_COLLECTION_NAME=<EMBEDDING_COLLECTION_NAME>
python app.py
docker build -t face_auth --build-arg SECRET_KEY=<SECRET_KEY> --build-arg ALGORITHM=<ALGORITHM> --build-arg MONGODB_URL_KEY=<MONGODB_URL_KEY> --build-arg DATABASE_NAME=<DATABASE_NAME> --build-arg USER_COLLECTION_NAME=<USER_COLLECTION_NAME> --build-arg EMBEDDING_COLLECTION_NAME=<EMBEDDING_COLLECTION_NAME> .
docker run -d -p 8000:8000 <IMAGEID OR IMAGENAME>
- Azure container Registry (ACR) for Docker image of project is stored
- Azure App Services for deploying the application
- GitHub Actions for CI/CD