Facial Authentication System

This application has not been deployed yet. I do not have access to any hosting services like AWS or Azure that provide GPU support for free. For testing, I created a microservice using Flask and Ngrok in Google Colab, where the Facenet Model was run and served using a REST API. This application can only be deployed once I get access to a free GPU service.

This program authenticates people using their faces and stores the data in a database. This application is being developed using Django and React. React is set up manually using Webpack and rendered using Django templates.

Set Up Instructions

1. Clone the repo.

$ git clone https://github.com/Kaushal-Dhungel/Facial-authentication.git

2. Install the dependencies

$ pip install -r requirements.txt

3. Go to front directory.

$ cd front

4. Install npm dependencies

$ npm i

6. Run the program.

$ python3 manage.py runserver

Running the facenet API

• Deploy the Flask app from facenet_api.py on a cloud service provider like AWS or Azure that provides GPU support.
• Copy the URL of the app and paste it in the TrainView and VerifyView of views.py inside the mainapp directory.

How To Use??

1. Register.

2. Create an event. An event holds the record of people belonging to a particular group or category. For example, in a school, an event can represent a particular grade, i.e., all the students from grade 1 will belong to one event, grade 2 to another event, and so on.

3. Train the model for each face belonging to a particular event. This can be done with a camera or by uploading a picture.

4. Create a subevent. Subevents are subsets of an event. A subevent will inherit all the records of its parent event. If January is an event, then each day of January can be a subevent. This way, the same record can be used for each day without the need to retrain the model.

5. Perform verification for each subevent.