Automatic Face Mask Detection Using Python

M.Madan Mohan; Sojasomanan; M.Kaleeswari

Author:

M.Madan Mohan, Sojasomanan, M.Kaleeswari

Published in

Journal of Science Technology and Research

( Volume , Issue )

Page No: 91-100

Volume , Issue

Article Type:

Published Date:

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Abstract

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning.

Keywords

Machine learning, R-CNN algorithm, MobilenetV2, Keras OpenCV. Face Mask Detection.

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ABSTRACT:

Automatic Face Mask Detection Using Python a solution was proposed to assist in minimizing COVID-19 transmission. Real-time mask detection was enabled using deep learning and computer vision techniques. A dataset containing images of individuals with and without face masks was utilized. The detection process was performed using OpenCV to process live webcam streams. Python, TensorFlow, and Keras were used for model development. Deep transfer learning techniques were applied to extract image features. These features were then processed using classical machine learning algorithms for classification. Multiple algorithms were compared to identify the most accurate and efficient model. By integrating the model with surveillance systems, real-time monitoring of mask compliance was made possible.However, The system was designed to generate alerts when individuals without masks were detected. Through this approach, an affordable and scalable public safety solution was provided.

INTRODUCTION:

Automatic Face Mask Detection Using Python was emphasized during the COVID-19 pandemic, when the global health emergency demanded effective preventive measures. The virus was known to spread even before symptoms appeared, mainly through coughing, sneezing, or speaking. As a result, face masks were recommended by the World Health Organization. Their use in public spaces was promoted to reduce virus transmission. In this context, a system was developed to detect face masks in real time using image processing. A webcam feed was processed using OpenCV, while Python, TensorFlow, and Keras were used for system development. A dataset of masked and unmasked images was applied for training. Feature extraction was performed using deep transfer learning methods. Therefore the Classical machine learning algorithms were combined for improved classification. Accuracy and speed were evaluated to select the most effective model.