scholarly journals Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

2021 ◽  
Vol 11 (11) ◽  
pp. 68-75
Author(s):  
Enrique Lee Huamaní ◽  
◽  
Lilian Ocares Cunyarachi
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Face Detection ◽  
Face Mask ◽  
Economic Activities ◽  
Facial Mask ◽  
The Face ◽  
The Individual ◽  
A Company ◽  
Selection Of

Due to the pandemic caused by Covid-19, daily life has changed significantly. For this reason, biosecurity measures have been implemented to prevent the spread of the virus as an effective way to reactivate economic activities. In this sense, the present paper focuses on real-time face detection as a measure of control at the entrance to an entity, thus avoiding the spread of the virus while recognizing the identity of workers despite the use of masks and thus reducing the risk of entry of individuals outside the organization. Therefore, the objective is to contribute to the security of a company through the application of machine learning methodology. The selection of methodology is justified due to the adaptation of the same according to the interests of this project. Consequently, algorithms were used in a progressive manner, obtaining as a result the control system that was intended, since each particularity of the face of the individual was recognized in relation to its corresponding identification. Finally, the results of this article benefit the security of organizations regardless of their field or sector. Keywords— Control, Detection, Facial Recognition, Facial Mask, Face recognition, Machine learning.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

scholarly journals COVIDSAVIOR: A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks

2021 ◽  
Vol 9 ◽  
Author(s):  
Sharnil Pandya ◽  
Anirban Sur ◽  
Nitin Solke
Keyword(s):  
Deep Learning ◽  
Body Temperature ◽  
Real Time ◽  
Sensor Fusion ◽  
Face Mask ◽  
Temperature Threshold ◽  
Mask Condition ◽  
Essential Information ◽  
The Individual ◽  
Scanning Mechanism

The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach—The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings—The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.

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

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

scholarly journals Vehicle Vacant Seat Identification and Mask Detection using Image processing

2021 ◽  
Vol 9 (VII) ◽  
pp. 118-121
Author(s):  
Mr. Shubham Ingole
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Face Detection ◽  
Detection Technique ◽  
Processing Technology ◽  
Raspberry Pi ◽  
Human Being ◽  
People Tracking ◽  
Software Application ◽  
The Face

This article describes the technique of real-time face detection, mask detection, and vacant seat available in the vehicle. There are so many technologies for finding seat availability in the vehicle. But image processing technology is very popular today. Face detection is part of image processing. It is used to find the face of a human being in a certain area. Face detection is used in many applications, such as facial recognition, people tracking or photography. In this paper, the face detection technique is used to detect the vacant seat availability in the vehicle and also to detect whether the passenger wear the mask on his face or not. The webcam is installed in the vehicle and connected with the Raspberry Pi 3 model B. When the vehicle leaves the station, the webcam will capture images of the passengers in the seating area. The webcam will be mounted on the vehicle. The images will be adjusted and enhanced to reduce noise made by the software application. The system obtains the maximum number of passengers in the vehicle that processes the images and then calculates the availability of seats in the vehicle. In covid-19 situation mask detection is necessary. so this system also used to detect the mask on face.

scholarly journals ANALYSIS OF HUMAN FACE RECOGNITION ALGORITHM USING PCA+FDIT IN IMAGE DATABASE FOR CRIME INVESTIGATION

2013 ◽  
Vol 4 (3) ◽  
pp. 788-796
Author(s):  
V. S. Manjula
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Skin Color ◽  
Back Propagation ◽  
Real Life ◽  
Hierarchical Architecture ◽  
Detection And Identification ◽  
The Face ◽  
Real Time Application

In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification  which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting.   Thus the proposed technique   is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.   It is very useful in identify a single person from a group of faces.   Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier.  Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.

scholarly journals Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

2021 ◽  
Vol 38 (6) ◽  
pp. 1875-1885
Author(s):  
Ruchi Jayaswal ◽  
Manish Dixit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Preventive Measure ◽  
Face Mask ◽  
Histogram Equalization ◽  
Test Accuracy ◽  
The Face ◽  
Novel Coronavirus ◽  
Face Detector

A novel coronavirus has spread over the world and has become an outbreak. This, according to a WHO report, is an infectious disease that aims to spread. As a consequence, taking precautions is the only method to avoid catching this virus. The most important preventive measure against COVID-19 is to wear a mask. In this paper, a framework is designed for face mask detection using a deep learning approach. This paper aims to predict a person having a mask or unmask and also presents a proposed dataset named RTFMD (Real-Time Face Mask Dataset) to accomplish this objective. We have also taken the RFMD dataset from the internet to analyze the performance of system. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied at the time of pre-processing to enhance the visual quality of images. Subsequently, Inceptionv3 model used to train the face mask images and SSD face detector model has been used for face detection. Therefore, this paper proposed a model CLAHE-SSD_IV3 to classify the mask or without mask images. The system is also tested at VGG16, VGG19, Xception, MobilenetV2 models at different hyperparameters values and analyze them. Furthermore, compared the result of the proposed dataset RTFMD with the RFMD dataset. Additionally, proposed approach is compared with the existing approach on Face Mask dataset and RTFMD dataset. The outcomes have obtained 98% test accuracy on this proposed dataset RTFMD while 97% accuracy on the RFMD dataset in real-time.

scholarly journals Assessment of COVID-19 risk and prevention effectiveness among spectators of mass gathering events

2021 ◽  
Author(s):  
Tetsuo Yasutaka ◽  
Michio Murakami ◽  
Yuichi Iwasaki ◽  
Wataru Naito ◽  
Masaki Onishi ◽  
...  
Keyword(s):  
Face Mask ◽  
Infection Risk ◽  
Exposure Model ◽  
Infection Prevalence ◽  
Mass Gathering ◽  
Local Infection ◽  
Need To Evaluate ◽  
The Face ◽  
Novel Coronavirus ◽  
The Individual

There is a need to evaluate and minimise the risk of novel coronavirus infections at mass gathering events, such as sports. In particular, to consider how to hold mass gathering events, it is important to clarify how the local infection prevalence, the number of spectators, the capacity proportion, and the implementation of preventions affect the infection risk. In this study, we used an environmental exposure model to analyse the relationship between infection risk and infection prevalence, the number of spectators, and the capacity proportion at mass gathering events in football and baseball games. In addition to assessing risk reduction through the implementation of various preventive measures, we assessed how face-mask-wearing proportion affects infection risk. Furthermore, the model was applied to estimate the number of infectors who entered the stadium and the number of newly infected individuals, and to compare them with actual reported cases. The model analysis revealed an 86%-95% reduction in the infection risk due to the implementation of face-mask wearing and hand washing. Among the individual measures, face-mask wearing was particularly effective, and the infection risk increased as the face-mask-wearing proportion decreased. A linear relationship was observed between infection risk at mass gathering events and the infection prevalence. Furthermore, the number of newly infected individuals was also dependent on the number of spectators and the capacity proportion independent of the infection prevalence, confirming the importance of considering spectator capacity in infection risk management. These results highlight that it is beneficial for organisers to ensure prevention compliance and to mitigate or limit the number of spectators according to the prevalence of local infection. Both the estimated and reported numbers of newly infected individuals after the events were small, below 10 per 3-4 million spectators, despite a small gap between these numbers.

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