FACE MASK AND SOCIAL DISTANCE DETECTION USING DEEP LEARNING TECHNIQUES

IARJSET ◽  
2021 ◽  
Vol 8 (9) ◽  
Author(s):  
Mrs.E. JOTHI
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Face Mask ◽  
Learning Techniques

MaskNet: CNN for Real-Time Face Mask Detection Based on Deep Learning Techniques

2021 ◽  
pp. 87-97
Author(s):  
Ismail Nasri ◽  
Mohammed Karrouchi ◽  
Hajar Snoussi ◽  
Abdelhafid Messaoudi ◽  
Kamal Kassmi
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Face Mask ◽  
Learning Techniques

scholarly journals Face Mask Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1520-1523
Keyword(s):  
Deep Learning ◽  
Face Mask ◽  
Video Stream ◽  
Still Images ◽  
Railway Stations ◽  
Health Authorities ◽  
Safety And Health ◽  
Learning Techniques ◽  
The Face ◽  
The Right

Businessesare constantly overhauling their existing infrastructure and processes to be more efficient, safe, and usable for employees, customers, and the community. With the ongoing pandemic, it's even more important to have advanced applications and services in place to mitigate risk. For public safety and health, authorities are recommending the use of face masks and coverings to control the spread of Coronovirus. The COVID-19 pandemic is devastation to themankind irrespective of caste, creed, gender, and religion. Using a face mask can undoubtedly help in managing the spread of the virus. COVID-19 face mask detector uses deep learning techniques to successfully test whether a person is wearing a face mask or not. Using a deep learning method called Convolutional Neural Network, got an accuracy of 98.6 %. It can work with still images and also works with a live video stream. Cases in which the mask is improperly worn are when the nose and mouth are partially covered is considered as the mask is not worn. Our face mask identifier is the least complex in structure and gives quick results and hence can be used in CCTV footage to detect whether a person is wearing a mask perfectly so that he does not pose any danger to others. Mass screening is possible and hence can be used in crowded places like railway stations, bus stops, markets, streets, mall entrances, schools, colleges, etc. By monitoring the placement of the face mask on the face, we can make sure that an individual wears it the right way and helps to curb the scope of the virus

scholarly journals Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247440
Author(s):  
Adina Rahim ◽  
Ayesha Maqbool ◽  
Tauseef Rana
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Time Of Flight ◽  
Absolute Error ◽  
Face Mask ◽  
Low Light ◽  
Environmental Cleaning ◽  
Social Distancing ◽  
Safety Distance ◽  
Tof Camera

The purpose of this work is to provide an effective social distance monitoring solution in low light environments in a pandemic situation. The raging coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has brought a global crisis with its deadly spread all over the world. In the absence of an effective treatment and vaccine the efforts to control this pandemic strictly rely on personal preventive actions, e.g., handwashing, face mask usage, environmental cleaning, and most importantly on social distancing which is the only expedient approach to cope with this situation. Low light environments can become a problem in the spread of disease because of people’s night gatherings. Especially, in summers when the global temperature is at its peak, the situation can become more critical. Mostly, in cities where people have congested homes and no proper air cross-system is available. So, they find ways to get out of their homes with their families during the night to take fresh air. In such a situation, it is necessary to take effective measures to monitor the safety distance criteria to avoid more positive cases and to control the death toll. In this paper, a deep learning-based solution is proposed for the above-stated problem. The proposed framework utilizes the you only look once v4 (YOLO v4) model for real-time object detection and the social distance measuring approach is introduced with a single motionless time of flight (ToF) camera. The risk factor is indicated based on the calculated distance and safety distance violations are highlighted. Experimental results show that the proposed model exhibits good performance with 97.84% mean average precision (mAP) score and the observed mean absolute error (MAE) between actual and measured social distance values is 1.01 cm.

scholarly journals Real Time Face Mask Identification using AI-Deep Learning Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 1424-1432
Author(s):  
Aishwarya. B. K
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Virus Disease ◽  
Face Mask ◽  
Primary Objective ◽  
Working Environment ◽  
Corona Virus ◽  
Learning Techniques ◽  
Deep Learning Neural Network ◽  
Safe Working

The COVID - 19 pandemic is devastating mankind irrespective of caste, creed, gender, and religion. Contribution of each individual to constrain the expansion of the corona- virus. Is a primary objective/Fundamental duties as a responsible individual to Use a face mask can undoubtedly help in managing the spread of the virus. COVID - 19 face mask Detector uses or owns Facemask net, deep learning techniques to successfully test whether a person is with wearing a face mask or not. In this project we are working on “FACE MASK IDENTIFICATION USING AI DEEP LEARNING NEURAL NETWORK”. The end of 2019 witnessed the outbreak of Corona virus Disease 2019 (COVID-19), which has continued to be the cause of plight for millions of lives and businesses even in 2020. As the world recovers from the pandemic and plans to return to a state of normalcy, there is a wave of anxiety among all individuals, especially those who intend to resume in-person activity. Studies have proved that wearing a face mask significantly reduces the risk of viral transmission as well as provides a sense of protection. However, it is not feasible to manually track the implementation of this policy. Technology holds the key here. We are using a Deep Learning based system that can detect instances where face masks are not used properly. Our system consists of a faster region-based Convolution Neural Network (FRCNN) architecture capable of detecting masked and unmasked faces and can be integrated with preinstalled CCTV cameras. This will help track safety violations, promote the use of face masks, and ensure a safe working environment.

scholarly journals Video Analytics Solution for COVID-19

2020 ◽  
Vol 6 (12) ◽  
pp. 141-146
Author(s):  
Prerna Gupta Dr. Bhoomi Gupta and Vandana Choudhary
Keyword(s):  
Deep Learning ◽  
Mass Screening ◽  
Analytic Solution ◽  
Face Mask ◽  
Video Analytics ◽  
Entrance Exam ◽  
Corona Virus ◽  
Learning Techniques ◽  
The Face ◽  
Danger To Others

Amid the global crisis of the Corona virus pandemic, new demands have emerged in the market which uses Video Analytics for finding solutions to halt the transmission of the Virus. The COVID - 19 pandemic is devastating mankind irrespective of caste, creed, gender, and religion. Until a vaccine is discovered, we should do our bit to constrain the expanse of the corona virus. Using a face mask can undoubtedly help in managing the spread of the virus. The face mask detector, a video analytic solution uses MobileNetV2 model, deep learning techniques to successfully test whether a person is wearing a face mask or not. The face mask identifier is least complex in structure and gives quick results and hence can be used in CCTV footage to detect whether a person is wearing a mask perfectly so that he does not pose any danger to others. Mass screening is possible with video analytics and hence can be used in crowded places like Airports, Hospitals Entrance Exam Centers, Schools and Colleges.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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