Benefits of Face mask detection

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Covid-19 Face Mask Recognition using Live Camera and Face Mask Detection Using TensorFlow and Keras

Mihir Patil1, Pratik Tiwari2, Rahul Sonkamble3 1, 2, 3MIT School of Engineering

Abstract: The COVID-19 epidemic has so quickly affected our daily lives and disrupted trade and movement. Wearing a protective face mask has become a new trend. In the near future, more and more public service providers will be asking clients to wear the mask properly to benefit their services. Therefore, the discovery of a face mask has become an important task of helping the international community. This paper introduces a simplified way to achieve this goal using basic machine learning packages such as TensorFlow, Keras. The proposed method detects the surface in the image correctly and indicates whether it has a mask on it or not. As a security guard, it can also detect faces and moving masks. The method achieves 95.77% accuracy and 94.58% respectively for different data sets. We evaluated improved parameter values using the Sequential Convolutional Neural Network model to determine the presence of the mask correctly without causing excessive alignment.

I. INTRODUCTION

According to the World Health Organization (WHO) official Status Report - 205, coronavirus 2019 (COVID-19) has infected more than 20 million people worldwide causing more than 0.7 million deaths [1]. People with COVID19 have had a wide range of reported symptoms - from minor manifestations to serious illness. Respiratory problems such as shortness of breath or difficulty breathing are one of them. Adults with pneumonia may be more prone to COVID-19 complications as they appear to be at higher risk [2]. Other common human coronaviruses that infect humans worldwide are 229E, HKU1, OC43, and NL63. Prior to the demise of humans, viruses such as the 2019-nCoV, SARS-CoV, and MERS-CoV infect animals and turn them into human coronaviruses [3]. People with respiratory problems can expose anyone (close to them) to a contagious bead. Circumcision of an unclean person can cause human infections as droplets that carry the virus may reach nearby areas. To prevent certain respiratory infections, including COVID-19, wearing a clinical mask is very necessary. The public should be aware that you must wear a mask to control the source or dislike COVID-19. Points may be of interest to the use of masks in mitigation the risk of injury from a dangerous person during “pre-symptomatic symptoms” and stigma against apostates who wear a mask to prevent the spread of the virus. The WHO emphasizes the prioritization of medical masks and respirators for health care assistants [4]. Therefore, the discovery of a face mask has become an important activity in today's world society. Finding a face mask involves finding the location of the face and finding out if you have a mask. The problem is with the acquisition of a common object to determine the categories of objects. Phase identification is associated with the division of a particular business group namely Face. It has many applications, such as automatic driving, education, surveillance, and more [5]. This paper introduces a simplified method to achieve the above goal using basic Machine Learning (ML) packages such as TensorFlow, Keras. The other paper is organized as follows: Phase II examines the related function associated with the acquisition of a face mask. Section III discusses the type of database used. Phase IV introduces the details of the packages integrated into the proposed model. Section V gives an idea of our approach. The results of the evaluation and analysis are reported in section VI. Section VII concludes and draws a line towards future activities.

II. RELATED WORK

On the way to find the face, the face is found in a picture that has a few features in it. According to [21], facial recognition studies require facial recognition, facial monitoring, and posture measurement. Looking at the picture alone, the challenge is to see the face in the picture. Face detection is a difficult task because the face changes size, shape, color, etc. and does not change. It becomes a difficult task of blurring an image blocked by something other than the camera, and so on. Authors in [22] thinks that the discovery of an invisible face comes with two major challenges: 1) the unavailability of a very strong database containing both covered or uncovered faces, and 2) extraction without the appearance of a face in a covered area.