ACU CS 111 - A Review on COVID-19 Face Mask Detection

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_____________________________________________________________________________________________________ *Corresponding author: E-mail: [email protected]; Journal of Pharmaceutical Research International 33(35B): 39-45, 2021; Article no.JPRI.70560 ISSN: 2456-9119 (Past name: British Journal of Pharmaceutical Research, Past ISSN: 2231-2919, NLM ID: 101631759) A Review on COVID-19 Face Mask Detection using CNN Kavita R. Singh1*, Shailesh D. Kamble1, Samiksha M. Kalbande1 and Punit Fulzele2 1Computer Science and Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India. 2Department of Pedodontics, Sharad Pawar Dental College; Secretary, Research and Development, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Sawangi, Wardha, India. Authors’ contributions This work was carried out in collaboration among all authors. All authors read and approved the final manuscript. Article Information DOI: 10.9734/JPRI/2021/v33i35B31896 Editor(s): (1) Dr. Aurora Martínez Romero, Juarez University, Mexico. (2) Prof. John Yahya I. Elshimali, Drew University of Medicine and Science, USA. (3) Dr. Ana Cláudia Coelho, University of Trás-os-Montes and Alto Douro, Portugal. Reviewers: (1) Ihab Layth Hussein Alsammak, Directorate General of Education of Karbala, Iraq. (2) Megha Kamble, LNCT Excellence, India. (3) Ghaidaa A. Hafedh Jaber, University of Babylon, Iraq. Complete Peer review History: Received 04 May 2021 Accepted 05 July 2021 Published 07 July 2021 ABSTRACT The World Health Organization claims (WHO),Corona Viruses the COVID-19 pandemic is causing a nationwide crisis, wearing a mask on a face in public places is an effective protection measure. The COVID-19 pandemic forced governments all over the world to implement quarantine measures in order to deter virus spread. Reports suggest that the risk of transmission is clearly minimized by wearing face masks when at work. An effective and economic approach to the use of AI in a manufacturing setting to build a secure environment. Using a face mask detection dataset, we will use Open CV to perform real-time face detection from a live stream from our webcam. Using Keras, Python, Tensorflow and Open CV, and, it will build a COVID-19 face mask detector with computer vision. Using computer vision and CNN, I aim to decide whether or not the person in the image or video streaming is wear a mask. Keywords: Deep learning; COVID-19 dataset; open Cv; tensorflow; CNN. Review ArticleSingh et al.; JPRI, 33(35B): 39-45, 2021; Article no.JPRI.70560 40 1. INTRODUCTION The COVID-19 corona virus pandemic is causing a worldwide health epidemic.As a result, the World Health Organization (WHO) recommends wearing a facemask in public. The planet has been seriously infected by the coronavirus outbreak of 2019.One of the main methods of safety for individuals to wear masks in public places and as well maintain social distancing during this crises. COVID-19's accelerated dissemination in 2020 caused the World Health Organization (WHO) to call it a national epidemic. In computer vision and digital image processing, face recognition has become a very common challenge. Face masks are becoming more common in public as a result of the global COVID-19 corona virus outbreak. Public used to wear a mask on face to shield themselves from airborne diseases / air pollution until COVID-19. Scientists have shown that wearing a mask on face acts to inhibit the transmission of COVID-19. COVID-19 is a modern epidemic virus that has infiltrated human health in the last century (known as the corona virus). The rapid spread of COVID-19 in 2020 impelled the WHO to announce COVID-19 to be a global epidemic. The corona virus epidemic has resulted in extraordinary levels of global scientific collaboration. In a variety of ways, Deep Learning and machine learning-based Artificial Intelligence can aid in the fight against COVID-19. Machine learning helps scientists and clinicians to evaluate the spread of COVID-19 in vast volumes, to serve as an early warning tool for potential pandemics, and to categorize species that are threatened. Provision of healthcare needs investment in order to combat and forecast emerging diseases for developing technologies i.e. IoT, artificial intelligence, large data and mechanical learning. 1.1 Tensorflow TensorFlow is a open source free online software library that covers a range of data flow and differentiated programming practices. Google’s second-generation framework, Tensorflow, is used for both research and growth at the company. TensorFlow supports 64-bit platforms like Ios and Android, like Linux, MacOS, Windows and smart computing systems. Its modular design enables the efficient use of machines across a wide range of platforms (TPUs, GPUs, CPUs), from desktops, server clusters and smartphones to edge computers. The expression TensorFlow refers to the operations performed on multidimensional data panels known as tensors by such neural networks. During the Google I/O Conference in June2016, Jeff Dean announced that there were 1,500 TensorFlow repositories on GitHub, but only 5 were from Google. 1.2 Keras Keras is a human-centric API, not a robot-centric one. By providing reliable and fast APIs and reducing the number of users tasks required for normal usage, Keras follows best practices in cognitive stress reduction and provides understandable and actionable error messages including general documentation and user tutorials. Keras provides a range of iterations for widely used building blocks of neural networks, such as objectives, layers, activation functions, optimizers and a series of image and text data processing techniques, making deep neural code writing simpler. GitHub hosts the framework, and the mutual aid forums include a GitHub topic tab and Slack site.Keras is an easy-to-use, deep learning library for Theano or TensorFlow. 1.3 OpenCV GitHub hosts the framework, and the mutual aid forums include a GitHub topic tab and Slack site. OpenCV was developed to provide

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