An AI and Computer Vision-based Face Mask Recognition & Detection System

The new pandemic of (Coronavirus Disease-2019) COVID-19 continues to spread worldwide. Every potential sector is experiencing a decline in growth. (World Health Organization) WHO suggests that Wearing Face Mask can reduce the impact of COVID-19. So, This Paper Proposed a system that controls the growth of COVID-19 by finding individuals who don't wear masks in populated areas like malls, markets where all public places are under surveillance with closed-circuit television cameras (CCTV). When a person without a mask is found, the corresponding authority is informed by the CCTV network. And it can calculate the number of people that do not wear the mask and emit an audible signal to inform the authority. A deep learning module is trained on a dataset composed of images of people wearing different types of masks and people without masks collected from various sources. It also contains some confusing images that help the model to achieve greater precision than other models. This model will use the dataset to build a COVID-19 face mask detector with computer vision using Computer Vision. This approach allowed extracting even the details from the pixels

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Artificial Intelligence Based Face Mask Detection System

Volume 5 - 2020, Issue 9 - September - International Journal of Innovative Science and Research Technology ◽

10.38124/ijisrt20aug410 ◽

2020 ◽

Vol 5 (8) ◽

pp. 467-470

Author(s):

Vivek Kumar Pandey

Keyword(s):

Artificial Intelligence ◽

Computer Vision ◽

Detection System ◽

Face Mask ◽

Recreational Activities ◽

Human Tendency ◽

The Face ◽

The Individual ◽

Day By Day ◽

Made In

With the advent of COVID-19 pandemic, use of mask is mandatory as per WHO/ ICMR guidelines to avert spread of CORONA virus. The post lockdown period has seen increase in cases day by day as people have now stepped out of their home to resume their work and recreational activities. Wearing mask all the time has still not found an enduring place in our day to day routine practices. It is a natural human tendency to be complacent and to remove mask while talking, working or after prolong use just use to relax and breathe properly. Thus not only risking own life but also of others who might have come in contact with the person during the period when he/she was not wearing mask. Presently the inspection of people with/ without mask is being done manually and visually by sentries/ guards present at entry/ exit points. Guards/ Sentries cannot be stationed at every place to keep a check on such people who remove their mask and roam around without restraint once they have been scrutinized at the entry gate. In the proposed system, efforts have been made in inspecting people with/ without mask automatically with the help of Computer vision and Artificial Intelligence. This module detects the face of the individual, identifies whether he/she is wearing mask or not and raises an alarm if the person is detected without wearing mask.

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Face Mask Detection System

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38855 ◽

2021 ◽

Vol 9 (11) ◽

pp. 639-642

Author(s):

Prof. A. T. Sonwane

Keyword(s):

Neural Network ◽

Image Processing ◽

Computer Vision ◽

Image Analysis ◽

Detection System ◽

Face Mask ◽

Public Places ◽

Protection Method ◽

The World ◽

Public Areas

Abstract: There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. Coronavirus disease 2019 has affected the world seriously. One major protection method for people is to wear masks in public areas. The risk of transmission is highest in public places. However, there are only a few research studies about face mask detection based on image analysis. This paper aims to present a review of various methods and algorithms used for human recognition with a face mask. The proposed system to classify face mask detection using COVID-19 precaution both in images and videos using convolution neural network, TensorFlow and OpenCV to detect face masks on people. This system has various applications at public places, schools, etc. where people need to be detected with the presence of a face mask and recognize them and help society. Keywords: COVID-19, Tensorflow, OpenCV, Face Mask, Image Processing, Computer Vision

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The Use of Closed-Circuit Television and Video in Suicide Prevention: Narrative Review and Future Directions

JMIR Mental Health ◽

10.2196/27663 ◽

2021 ◽

Vol 8 (5) ◽

pp. e27663

Author(s):

Sandersan Onie ◽

Xun Li ◽

Morgan Liang ◽

Arcot Sowmya ◽

Mark Erik Larsen

Keyword(s):

Computer Vision ◽

Early Intervention ◽

Suicide Attempt ◽

Detection System ◽

Automated Detection ◽

Video Data ◽

Narrative Review ◽

Closed Circuit ◽

Closed Circuit Television ◽

Area Of Interest

Background Suicide is a recognized public health issue, with approximately 800,000 people dying by suicide each year. Among the different technologies used in suicide research, closed-circuit television (CCTV) and video have been used for a wide array of applications, including assessing crisis behaviors at metro stations, and using computer vision to identify a suicide attempt in progress. However, there has been no review of suicide research and interventions using CCTV and video. Objective The objective of this study was to review the literature to understand how CCTV and video data have been used in understanding and preventing suicide. Furthermore, to more fully capture progress in the field, we report on an ongoing study to respond to an identified gap in the narrative review, by using a computer vision–based system to identify behaviors prior to a suicide attempt. Methods We conducted a search using the keywords “suicide,” “cctv,” and “video” on PubMed, Inspec, and Web of Science. We included any studies which used CCTV or video footage to understand or prevent suicide. If a study fell into our area of interest, we included it regardless of the quality as our goal was to understand the scope of how CCTV and video had been used rather than quantify any specific effect size, but we noted the shortcomings in their design and analyses when discussing the studies. Results The review found that CCTV and video have primarily been used in 3 ways: (1) to identify risk factors for suicide (eg, inferring depression from facial expressions), (2) understanding suicide after an attempt (eg, forensic applications), and (3) as part of an intervention (eg, using computer vision and automated systems to identify if a suicide attempt is in progress). Furthermore, work in progress demonstrates how we can identify behaviors prior to an attempt at a hotspot, an important gap identified by papers in the literature. Conclusions Thus far, CCTV and video have been used in a wide array of applications, most notably in designing automated detection systems, with the field heading toward an automated detection system for early intervention. Despite many challenges, we show promising progress in developing an automated detection system for preattempt behaviors, which may allow for early intervention.

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Image Detection System Using Image Processing

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36190 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 5356-5362

Author(s):

Mohini Gawande

Keyword(s):

Image Processing ◽

Computer Vision ◽

Detection System ◽

Estimation Method ◽

Scale Invariant ◽

Matrix Estimation ◽

Speeded Up Robust Features ◽

Invariant Features ◽

Marketing Analysis ◽

New Strategies

The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efﬁciently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.

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