Face Authentication System

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.

Project Archietecture

Run the Application

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.

Step 1-: Clone the Repository

git clone https://github.com/Deep-Learning-01/Deep-Authenticator.git

Step 2-: Creat conda environment

conda create -p ./env python=3.8.13 -y

Step 3-: Activate Conda environment

conda activate ./env

Step 4-: Install requirements

pip install -r requirements.txt

Step 5-: Export the environment variable

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>

.env file

SECRET_KEY=KlgH6AzYDeZeGwD288to79I3vTHT8wp7
ALGORITHM=HS256
DATABASE_NAME='UserDatabase'
USER_COLLECTION_NAME='User'
EMBEDDING_COLLECTION_NAME='Embedding'
MONGODB_URL_KEY=

Step 6-: Run the application server

python app.py

Run Locally

Build the Docker Image

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> .

Run the Docker Image

docker run -d -p 8000:8000 <IMAGEID OR IMAGENAME>

Deployment to Azure

Services used

Azure container Registry (ACR) for Docker image of project is stored
Azure App Services for deploying the application
GitHub Actions for CI/CD

surajbilgi / deep-authenticator-face Goto Github PK

deep-authenticator-face's Introduction