scholarly journals State model based face mask detection

2018 ◽  
Vol 7 (2.22) ◽  
pp. 35
Author(s):  
Kavitha M ◽  
Mohamed Mansoor Roomi S ◽  
K Priya ◽  
Bavithra Devi K
Keyword(s):  
Face Mask ◽  
Search Space ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
State Model ◽  
Face Region ◽  
Automatic Teller Machine ◽  
Foreground Region ◽  
The Face ◽  
Surveillance Mechanism

The Automatic Teller Machine plays an important role in the modern economic society. ATM centers are located in remote central which are at high risk due to the increasing crime rate and robbery.These ATM centers assist with surveillance techniques to provide protection. Even after installing the surveillance mechanism, the robbers fool the security system by hiding their face using mask/helmet. Henceforth, an automatic mask detection algorithm is required to, alert when the ATM is at risk. In this work, the Gaussian Mixture Model (GMM) is applied for foreground detection to extract the regions of interest (ROI) i.e. Human being. Face region is acquired from the foreground region through  the torso partitioning and applying Viola-Jones algorithm in this search space. Parts of the face such as Eye pair, Nose, and Mouth are extracted and a state model is developed to detect  mask.  

The Target Motion Detection Algorithm Based on Gauss Mixture Model

2012 ◽  
Vol 468-471 ◽  
pp. 1421-1425 ◽  
Author(s):  
Qiu Chan Bai ◽  
Chun Xia Jin ◽  
Ding Li Yang ◽  
Ma Hua Wang
Keyword(s):  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Small Time ◽  
Moving Target ◽  
Background Region ◽  
Background Reduction ◽  
Foreground Region ◽  
Background Extraction ◽  
Gauss Mixture ◽  
Background Image

Background reduction technique not only has the characteristics of pixels identify changes and small time complexity, but also can provide better detection results. The paper puts forward the running average method constructing background image by Gaussian mixture mode detecting background region and foreground region, and then adopting background reduction realizes the detection of moving target. Experimental results show that the algorithm effectively may realize background extraction and updating, and then completely and accurately detects moving targets. The algorithm has been achieved good results in the video vehicle detection.

Research on Fast Face Detection Algorithm in Complex Background

2014 ◽  
Vol 1030-1032 ◽  
pp. 1779-1782
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Prior Knowledge ◽  
Face Detection ◽  
Fast Algorithm ◽  
Vertical Direction ◽  
Detection Algorithm ◽  
Color Information ◽  
Human Eye ◽  
Face Region ◽  
Complex Background ◽  
The Face

This paper presents a fast algorithm for face detection in complex background, in which image color information is used first, project upper part of the partition of face in the gray image to horizontal and vertical direction. Determine the eyes positions by the minimum value, and scope the human eye in the face of prior knowledge to judge and adjust the face region.

scholarly journals Mask Detection and Tracing System

2021 ◽  
pp. 406-412
Author(s):  
Sanket Shete ◽  
Kiran Tingre ◽  
Ajay Panchal ◽  
Vaibhav Tapse ◽  
Prof. Bhagyashri Vyas
Keyword(s):  
Neural Networks ◽  
Face Detection ◽  
Large Scale ◽  
Face Mask ◽  
Detection Algorithm ◽  
Detection Task ◽  
Loss Of Face ◽  
Satisfactory Performance ◽  
Training Samples ◽  
The Face

Covid19 has given a new identity for wearing a mask. It is meaningful when these masked faces are detected accurately and efficiently. As a unique face detection task, face mask detection is much more difficult because of extreme occlusions which leads to the loss of face details. Besides, there is almost no existing large-scale accurately labelled masked face dataset, which increase the difficulty of face mask detection. The system encourages to use CNN-based deep learning algorithms which has done vast progress towards researches in face detection In this paper, we propose novel CNN-based method which is formed of three convolutional neural networks to detect face mask. Besides, because of the shortage of face masked training samples, we propose a new dataset called” face mask dataset” to finetune our CNN models. We evaluate our proposed face mask detection algorithm on the face mask testing set, and it achieves satisfactory performance

Research and Improvement on the Algorithm of Face Region Detection Based on Skin Color Model

2014 ◽  
Vol 543-547 ◽  
pp. 2702-2705
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Algorithm ◽  
Face Image ◽  
Color Model ◽  
Region Detection ◽  
Face Region ◽  
The Face ◽  
Skin Color Model ◽  
Better Than

In face image with complex background, the CbCr skin color region will have offset when considering the illumination change. Therefore, the non-skin color pixels which luminance is less than 80 will be mistaken as skin color pixels and the skin color pixels which luminance is greater than 230 will be mistaken as non-skin color pixels. In order to reduce the misjudgments, an improved skin color model of nonlinear piecewise is put forward in this paper. Firstly, the skin color model of non-piecewise is analyzed and the experimental results show that by this model there is an obvious misjudgment in face detection. Then the skin color model of nonlinear piecewise is mainly analyzed and is demonstrated by mathematics method. A large number of training results show that the skin color model of nonlinear piecewise has better clustering distribution than the skin color model of non-piecewise. At lastly, the face detection algorithm adopting skin color model of nonlinear piecewise is analyzed. The results show that this algorithm is better than the algorithm adopting skin color model of non-piecewise and it makes a good foundation for the application of face image.

scholarly journals Automatic Approach for Mask Detection Effective: For COVID-19

2021 ◽  
Author(s):  
Debajyoty Banik ◽  
Saksham Rawat Rawat ◽  
Aayush Thakur ◽  
Pritee Parwekar ◽  
Suresh Chandra Satapathy
Keyword(s):  
Region Of Interest ◽  
Face Mask ◽  
Working Environment ◽  
Public Places ◽  
Detection Model ◽  
Face Region ◽  
Sense Of Security ◽  
Improper Use ◽  
The Face ◽  
Dual Stage

Abstract The outbreak of Coronavirus Disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a Deep Learning-based system that can detect instances of improper use of face masks. A dual-stage Convolutional Neural Network (CNN)architecture is used in our system to recognie masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. This paper will automate the tasks of mask detection in public places when incorporated with CCTV cameras and will alert the system manager when a person without mask or wearing incorrect mask tries to enter. This paper includes multi face detection model which has the potential to target and identify a group of people whether they are wearing masks or not. We tried to collect various facial pictures and tried to identify the face Region of Interest (ROI), and then we separated it. Applying facial milestones, to permit the restriction the eyes, nose, mouth, and so. face was then completed and we tried to detect the presence of mask. To prepare a custom face cover locator, breaking our venture into two unmistakable stages was required, each with its own separate sub-steps. 1. Preparing: Here, stacking our face veil discovery dataset from plate, preparing a model on this dataset, and afterward serializing the face cover locator to circle was the focus. 2. Sending: Once the face veil identifier is prepared, the accompanying advance of stacking the cover finder, performing face recognition, and afterward characterizing each face as with veil or without veil, can be executed.

scholarly journals Remote Monitoring of Patient Respiration with Mask Attachment—A Pragmatic Solution for Medical Facilities

Inventions ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 81
Author(s):  
Vivian Ci Ai Koh ◽  
Yi Yang Ang ◽  
Wee Ser ◽  
Rex Xiao Tan
Keyword(s):  
Remote Monitoring ◽  
Healthcare Professionals ◽  
Vital Signs ◽  
Face Mask ◽  
Detection Algorithm ◽  
Data Collection Process ◽  
Medical Facilities ◽  
The Face ◽  
The Mean ◽  
Healthy Participants

Remote monitoring of vital signs in infectious patients minimizes the risks of viral transmissions to healthcare professionals. Donning face masks could reduce the risk of viral transmissions and is currently practiced in medical facilities. An acoustic-sensing device was attached to face masks to assist medical facilities in remotely monitoring patients’ respiration rate and wheeze occurrence. Usability and functionality studies of the modified face mask were evaluated on 16 healthy participants. Participants were blindfolded throughout the data collection process. Respiratory rates of the participants were evaluated for one minute. The wheeze detection algorithm was assessed by playing 176 wheezes and 176 normal breaths through a foam mannequin. No discomfort was reported from the participants who used the modified mask. The mean error of respiratory rate was found to be 2.0 ± 1.3 breath per minute. The overall accuracy of the wheeze detection algorithm was 91.9%. The microphone sensor that was first designed to be chest-worn has been proven versatile to be adopted as a mask attachment. The current findings support and suggest the use of the proposed mask attachment in medical facilities. This application can be especially helpful in managing a sudden influx of patients in the face of a pandemic.

Face detection in single and multiple images using different skin color models

2020 ◽  
Vol 13 ◽  
Author(s):  
Manpreet Kaur ◽  
Jasdev Bhatti ◽  
Mohit Kumar Kakkar ◽  
Arun Upmanyu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Input Image ◽  
Morphological Operations ◽  
Reliability Model ◽  
Multiple Images ◽  
Face Region ◽  
Bounding Box ◽  
The Face ◽  
Precision And Accuracy

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, in face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue ( Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect that the algorithm been proposed performs very well in each model for detecting the faces in single and multiple images and the reliability model provides the best fit by analyzing the precision and accuracy. Moreover Discussion: The calculated results show that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images. Also, the evaluated results by this paper provides the better testing strategies that helps to develop new techniques which leads to an increase in research effectiveness. Conclusion: The calculated value of all parameters is helpful for proving that the proposed algorithm has been performed very well in each model for detecting the face by using a bounding box around the face in single as well as multiple images. The precision and accuracy of all three models are analyzed through the reliability model. The comparison calculated in this paper reflects that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images.

scholarly journals Examining the interplay between face mask usage, asymptomatic transmission, and social distancing on the spread of COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adam Catching ◽  
Sara Capponi ◽  
Ming Te Yeh ◽  
Simone Bianco ◽  
Raul Andino
Keyword(s):  
Social Distance ◽  
Large Fraction ◽  
Real Life ◽  
Virus Transmission ◽  
Face Mask ◽  
Social Distancing ◽  
Viral Spread ◽  
The Face ◽  
Asymptomatic Individuals ◽  
High Virus

AbstractCOVID-19’s high virus transmission rates have caused a pandemic that is exacerbated by the high rates of asymptomatic and presymptomatic infections. These factors suggest that face masks and social distance could be paramount in containing the pandemic. We examined the efficacy of each measure and the combination of both measures using an agent-based model within a closed space that approximated real-life interactions. By explicitly considering different fractions of asymptomatic individuals, as well as a realistic hypothesis of face masks protection during inhaling and exhaling, our simulations demonstrate that a synergistic use of face masks and social distancing is the most effective intervention to curb the infection spread. To control the pandemic, our models suggest that high adherence to social distance is necessary to curb the spread of the disease, and that wearing face masks provides optimal protection even if only a small portion of the population comply with social distance. Finally, the face mask effectiveness in curbing the viral spread is not reduced if a large fraction of population is asymptomatic. Our findings have important implications for policies that dictate the reopening of social gatherings.

Effects of Face Mask Use on Objective and Subjective Measures of Thermoregulation During Exercise in the Heat

2021 ◽  
pp. 194173812110282
Author(s):  
Ayami Yoshihara ◽  
Erin E. Dierickx ◽  
Gabrielle J. Brewer ◽  
Yasuki Sekiguchi ◽  
Rebecca L. Stearns ◽  
...  
Keyword(s):  
Exercise Intensity ◽  
Thermal Sensation ◽  
Face Mask ◽  
Whole Body ◽  
Moderate Exercise ◽  
Physically Active ◽  
Time Points ◽  
The Face ◽  
And Control ◽  
Moderate Exercise Intensity

Background: While increased face mask use has helped reduce COVID-19 transmission, there have been concerns about its influence on thermoregulation during exercise in the heat, but consistent, evidence-based recommendations are lacking. Hypothesis: No physiological differences would exist during low-to-moderate exercise intensity in the heat between trials with and without face masks, but perceptual sensations could vary. Study Design: Crossover study. Level of Evidence: Level 2. Methods: Twelve physically active participants (8 male, 4 female; age = 24 ± 3 years) completed 4 face mask trials and 1 control trial (no mask) in the heat (32.3°C ± 0.04°C; 54.4% ± 0.7% relative humidity [RH]). The protocol was 60 minutes of walking and jogging between 35% and 60% of relative VO2max. Rectal temperature (Trec), heart rate (HR), temperature and humidity inside and outside of the face mask (Tmicro_in, Tmicro_out, RHmicro_in, RHmicro_out) and perceptual variables (rating of perceived exertion (RPE), thermal sensation, thirst sensation, fatigue level, and overall breathing discomfort) were monitored throughout all trials. Results: Mean Trec and HR increased at 30- and 60-minute time points compared with 0-minute time points, but no difference existed between face mask trials and control trials ( P > 0.05). Mean Tmicro_in, RHmicro_in, and humidity difference inside and outside of the face mask (ΔRHmicro) were significantly different between face mask trials ( P < 0.05). There was no significant difference in perceptual variables between face mask trials and control trials ( P > 0.05), except overall breathing discomfort ( P < 0.01). Higher RHmicro_in, RPE, and thermal sensation significantly predicted higher overall breathing discomfort ( r2 = 0.418; P < 0.01). Conclusion: Face mask use during 60 minutes of low-to-moderate exercise intensity in the heat did not significantly affect Trec or HR. Although face mask use may affect overall breathing discomfort due to the changes in the face mask microenvironment, face mask use itself did not cause an increase in whole body thermal stress. Clinical Relevance: Face mask use is feasible and safe during exercise in the heat, at low-to-moderate exercise intensities, for physically active, healthy individuals.

scholarly journals Effect of Mouth Mask and Face Shield on Speech Spectrum in Slovak Language

2021 ◽  
Vol 11 (11) ◽  
pp. 4829
Author(s):  
Vojtech Chmelík ◽  
Daniel Urbán ◽  
Lukáš Zelem ◽  
Monika Rychtáriková
Keyword(s):  
Speech Intelligibility ◽  
Face Mask ◽  
Suprasternal Notch ◽  
Lombard Effect ◽  
Slavic Languages ◽  
Female Speech ◽  
The Face ◽  
Frequency Components ◽  
Acoustic Filtering ◽  
Filtering Effect

In this paper, with the aim of assessing the deterioration of speech intelligibility caused by a speaker wearing a mask, different face masks (surgical masks, FFP2 mask, homemade textile-based protection and two kinds of plastic shields) are compared in terms of their acoustic filtering effect, measured by placing the mask on an artificial head/mouth simulator. For investigating the additional effects on the speaker’s vocal output, speech was also recorded while people were reading a text when wearing a mask, and without a mask. In order to discriminate between effects of acoustic filtering by the mask and mask-induced effects of vocal output changes, the latter was monitored by measuring vibrations at the suprasternal notch, using an attached accelerometer. It was found that when wearing a mask, people tend to slightly increase their voice level, while when wearing plastic face shield, they reduce their vocal power. Unlike the Lombard effect, no significant change was found in the spectral content. All face mask and face shields attenuate frequencies above 1–2 kHz. In addition, plastic shields also increase frequency components to around 800 Hz, due to resonances occurring between the face and the shield. Finally, special attention was given to the Slavic languages, in particular Slovak, which contain a large variety of sibilants. Male and female speech, as well as texts with and without sibilants, was compared.

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