Index

• Team
• Preface
• Tools and Technologies
• Prerequisites
• Mission
• Current Features
• Watch Our Demo
• Future Features

Team

• Cameron Beaulieu
• Evan Kilburn
• Brandon Ye
• Barrett Arbour

Preface

This project was created as part of the hackHer, a hackathon hosted by Queen's Unveristy Women in Computing in 2020. Our appplication was built to create a solution to a modern issue facing society, namely Covid-19.

Tools and Technologies

Languages

• Python 3.8.6
• JavaScript
• CSS
• HTML

Core Frameworks and Libraries

• Flask
• Tensorflow
• React
• Make Image Classifier
• Face Recognition 1.3.0

Prerequisites

Run the following before running the application:

• pip install -r requirements.txt
• pip install "tensorflow-hub[make_image_classifier]~=0.6"

Building the Model

Run the following command to train and build a new model. See https://github.com/tensorflow/hub/tree/master/tensorflow_hub/tools/make_image_classifier#basic-usage

make_image_classifier \
 --image_dir image_training_sets \
 --tfhub_module https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4 \
 --image_size 224 \
 --saved_model_dir new_model \
 --labels_output_file class_labels.txt \
 --tflite_output_file new_mobile_model.tflite \
 --summaries_dir log

Mission

Veil was developed in hopes of being able to identify those not wearing masks as to create a tool used to help stop the spread of Covid-19. While this was implemented in a web app as a proof of concept, configuring it to work with real time video capture and integrating it with security cameras would ultimately give business a way to identify those not abiding by mask laws.

Current Features

• Webcam image can be captured using laptop camera
• Image is processed to identify and isolate faces in seperate files
• Faces are processed through machine learning model to determine whether or not the person in the image is wearing a face mask
• The faces are displayed to the screen with an indication of whether or not they are wearing a face mask

Watch Our Demo

Demo Video

Future Features

• Train with a larger database to create a more accurate model that can handle more edge cases
• Change the image capturing to video capture allowing for real time calls to the model
• Train the model on improper mask use such as someone not covering their nose