/face-mask-detector

Detect mask on the faces of people on photographs, videos, and live video feed using OpenCV and TensorFlow.

Primary LanguageJupyter NotebookMIT LicenseMIT

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Face Mask Detector

Detect mask on a person's face on photos, videos and live camera feed.
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Table of Contents

About The Project

This is a Deep Learning Computer Vision project using Prajna Bhandary's dataset.
This project consists of data processing, model definition and model training and evaluation using tensorflow.

Built With

Getting Started

The project consists of three stages:

  1. Data Processing
  2. Model Definition
  3. Model training, and evaluation

The .ipynb (Jupyter Notebook) file contains the three stages of the project.

Data Processing

Data Processing refers to the analysis, manipulation/transformation of the dataset to obtain a usable form of the data.
This consists of data partitioning, tokenization, padding, etc.

Model Definition

Model definition refers to the process of choosing the models those are best suited for the dataset, along with the initial hyperparameters.

Model Training, and Evaluation

Model training refers to the process of fitting the models to the training data.
Model evaluation refers to the process of evaluating the process of the models using certain appropriate evaluation metrics and tuning the hyperparameters again based on the results.

It is an iterative process until required performance is obtained.

Usage

This model can be used to detect masks on faces to provide safety solutions for COVID-19 and other health issues. This has its various applications such as:

  • Automated door access control based on mask
  • Statistical observation of people wearing masks
  • Market Research
  • Social Distancing implementation

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

Soundar Murugan - @soundarzozm
E-Mail - soundarmurugan91@gmail.com

Project Link: https://github.com/soundarzozm/face-mask-detector

Acknowledgements