Facial Recognition Dashboard

A Weekend Project showing off Facial Recognition
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Table of Contents

• Table of Contents
• About The Project
  • Built With
    • Frontend
    • Backend
    • Deep Learning
• Getting Started
  • Prerequisites
  • Minimum Hardware Requirements
  • Installation
• Usage
• Changelog
• Roadmap
• Contributing
• License
• Contact
  • Animikh Aich
• Acknowledgements

About The Project

A portfolio style dashboard to show Face Detection and Recognition in action. This is also my first attempt at a full-stack deployment of a Deep Learning based project.

It does a few fundamental actions:

• The dashboard provides a UI for the user to interact with the underlying backend
• The pre-trained MTCNN based Face Detection detects faces and prompts the user to label them
• The pre-trained VGG Face extracts the facial features from the cropped faces
• When the user uploads the Query images, Detection and followed by Feature Extraction is done again for the query faces
• Finally, the similarity is between the labeled faces and the query faces are calculated using Cosine Distance Metric and the best match is displayed with a bounding box and name.

Built With

Below are the languages, libraries and frameworks used for this project

Frontend

• HTML
• CSS
• Bootstrap
• JavaScript

Backend

• Python
• Flask

Deep Learning

• Keras
• Tensorflow

Getting Started

Running the code to start the web-server is fairly simple. You can easily follow the steps.

Prerequisites

• Python 3
• Git Command Line

Minimum Hardware Requirements

• CPU: 2 Logical Cores (Threads)
• RAM: 4 GB
• Storage: 10 GB (Including Dependencies)
• OS: Linux, Windows, MacOS
• Internet: 2 Mbps

Installation

1. Clone the repo

git clone https://github.com/animikhaich/Facial-Recognition-Dashboard.git

2. Install Python packages

pip install -r requirements.txt

3. Run the Flask Server

python app.py

Usage

This was a weekend project for me. It's primary purpose is to serve as a demo of Facial Recognition. You can easily modify it as per your needs for a college project.

Changelog

See the Changelog.

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Animikh Aich

• LinkedIn: Animikh Aich
• Email: animikhaich@gmail.com
• Twitter: @AichAnimikh

Acknowledgements

• ipazc - MTCNN
• rcmalli - Keras VGGFace