Face Mask Detection And Alert System

The present scenario of COVID-19 demands an efficient face mask detection application. The main goal of the project is to implement this system at entrances of colleges, airports, hospitals, and offices where chances of spread of COVID-19 through contagion are relatively higher. Reports indicate tha

Project Title

Face Mask Detection And Alert System

Project Area of Specialization

Artificial Intelligence

Project Summary

The present scenario of COVID-19 demands an efficient face mask detection application. The main goal of the project is to implement this system at entrances of colleges, airports, hospitals, and offices where chances of spread of COVID-19 through contagion are relatively higher. Reports indicate that wearing face masks while at work reduces the risk of transmission. It is an object detection and classification problem with two different classes (Mask and Without Mask). A hybrid model using deep and classical machine learning for detecting face masks will be presented. A dataset is used to build this face mask detector using Python, OpenCV, TensorFlow, and Keras. While entering the place everyone should be scanned through a camera and ensure they have put on a mask. If anyone is found to be without a face mask, a beep alert will be generated. Also, the data of masked and unmask persons will be stored in an Excel file and the file will be sent to the administration through email. As all the workplaces are opening. The number of cases of COVID-19 is still getting registered throughout the country. If everyone follows the safety measures, then it can come to an end. Hence to ensure that people wear masks while coming to work we hope this module will help in detecting it.

Project Objectives

The main objectives of Face mask detection and alert system are:

• Detect People whether they are wearing masks or not
• Generate a sound alert when the mask is not detected
• Store details of masked and unmasked persons in an Excel file
• Email Excel file to administration

Project Implementation Method

We will use a Convolutional neural network but here is a little change, our idea to implement is after giving an input we will use mobilenet. After an image is processed is in array we will send it to the mobilenet, after that max-pooling and then flatten it and create a fully connected layer and get the output.

Benefits of the Project

Intelligent Alerts

Face mask detection and alert system are like its names suggest it generate a sound alert when the face mask is not detected. 

Facial Recognition

Facial recognition is used in a face mask detection system to detect a mask on the face of a person. So, when a face is detected then it searches for a face mask.

Camera Agnostic

Face mask detection and alert system work on images, videos, and live camera streams.

Easy Implementation

A face mask detection system is easy to implement and cost-effective.

No New Hardware Needed

A face mask detection system does not require kind of new and expensive hardware. Just a computer system and a camera

Technical Details of Final Deliverable

For this purpose, we have taken two datasets. One is with-mask and without-mask. With-mask contains 1915 images of masked male and female Asians and without-mask contains 1918 images. All the images used were taken from Kaggle, Google, and a few other open-source libraries.

We have used Image augmentation,  Image augmentation is a technique of applying different transformations to original images which results in multiple transformed copies of the same image. Each copy, however, is different from the other in certain aspects depending on the augmentation techniques you apply like shifting, rotating, flipping, etc. Applying these small amounts of variations on the original image does not change its target class but only provides a new perspective of capturing the object in real life. And so, we use it is quite often for building deep learning models.

We will use a Convolutional neural network to train the model but here is a little change, our idea to implement is after giving an input we will use mobilenet. After an image is processed is in array we will send it to the mobilenet, after that max-pooling and then flatten it and create a fully connected layer and get the output.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People, Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com