scholarly journals Face Reconstruction with Variational Autoencoder and Face Masks

2021 ◽  
Author(s):  
Rafael S. Toledo ◽  
Eric A. Antonelo
Keyword(s):  
Deep Learning ◽  
Face Mask ◽  
Loss Functions ◽  
High Dimensional ◽  
Learning Models ◽  
Face Reconstruction ◽  
Variational Autoencoder ◽  
The Face ◽  
Face Synthesis

Variational AutoEncoders (VAE) employ deep learning models to learn a continuous latent z-space that is subjacent to a high-dimensional observed dataset. With that, many tasks are made possible, including face reconstruction and face synthesis. In this work, we investigated how face masks can help the training of VAEs for face reconstruction, by restricting the learning to the pixels selected by the face mask. An evaluation of the proposal using the celebA dataset shows that the reconstructed images are enhanced with the face masks, especially when SSIM loss is used either with l1 or l2 loss functions. We noticed that the inclusion of a decoder for face mask prediction in the architecture affected the performance for l1 or l2 loss functions, while this was not the case for the SSIM loss. Besides, SSIM perceptual loss yielded the crispest samples between all hypotheses tested, although it shifts the original color of the image, making the usage of the l1 or l2 losses together with SSIM helpful to solve this issue.

scholarly journals IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3495
Author(s):  
Shabir Hussain ◽  
Yang Yu ◽  
Muhammad Ayoub ◽  
Akmal Khan ◽  
Rukhshanda Rehman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Face Mask ◽  
World Health ◽  
Rapid Screening ◽  
Screening System ◽  
Source Of Infection ◽  
The Face ◽  
Health Organization ◽  
Vaccine Distribution

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

scholarly journals Focused small-scale fisheries as complex systems using deep learning models

2021 ◽  
Vol 49 (2) ◽  
pp. 342-353
Author(s):  
Ricardo Cavieses-Núñez ◽  
Miguel A. Ojeda-Ruiz ◽  
Alfredo Flores-Irigollen ◽  
Elvia Marín-Monroy ◽  
Mirtha Lbañez-Lucero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Environmental Variables ◽  
Predictive Power ◽  
Small Scale ◽  
Learning Models ◽  
Small Scale Fisheries ◽  
Productive Activities ◽  
Lack Of Information ◽  
Management Measures ◽  
The Face

Small-scale fishing (SSF) is a relevant economic activity worldwide, so sustainable development will be essential to assure its contributions to food security, poverty alleviation, and healthy ecosystems. However, the wide diversity of fisheries, their complexity, and the lack of information limit the ability to propose/evaluate management measures and plans and their effects on communities and other productive activities. The state of Baja California Sur, Mexico, our study case, ranks as the third place in national fisheries production, possesses SSF fleets, has a wide variety of fisheries that share fishing areas, fishing seasons, and operating units. In this work, assuming SSF as a complex system were proposed deep learning models (DLM) to forecast the catch volumes, evaluate each input variable's importance, and find interactions. Environmental variables and catch fisheries were tested in the DLM to estimate their predictive power. Different DLM structures and parameters to find the optimal model was used. The variables that presented higher predictive power are the environmental variables with R = 0.90. Moreover, when used in combination with the catches from other areas, the performance of R = 0.95 is obtained. Using only the catches, the model has an R = 0.81. This model allows the use of variables that indirectly affect the system and demonstrates a useful tool to assess a complex system's state in the face of disturbances in its variables.

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 Autonomic Face Mask Detection with Deep Learning: an IoT Application

2021 ◽  
Author(s):  
◽  
V. H. Benitez-Baltazar
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Face Mask ◽  
World Health ◽  
Image Tracking ◽  
Critical Areas ◽  
Machine Learning Model ◽  
The World ◽  
The Face ◽  
Health Organization

A new and deadly virus known as SARS-CoV-2, which is responsible for the coronavirus disease (COVID-19), is spreading rapidly around the world causing more than 3 million deaths. Hence, there is an urgent need to find new and innovative ways to reduce the likelihood of infection. One of the most common ways of catching the virus is by being in contact with droplets delivered by a sick person. The risk can be reduced by wearing a face mask as suggested by the World Health Organization (WHO), especially in closed environments such as classrooms, hospitals, and supermarkets. However, people hesitate to use a face mask leading to an increase in the risk of spreading the disease, moreover when the face mask is used, sometimes it is worn in the wrong way. In this work, an autonomic face mask detection system with deep learning and powered by the image tracking technique used for the augmented reality development is proposed as a mechanism to request the correct use of face masks to grant access to people to critical areas. To achieve this, a machine learning model based on Convolutional Neural Networks was built on top of an IoT framework to enforce the correct use of the face mask in required areas as it is requested by law in some regions.

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 Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach

2021 ◽  
Vol 3 (2) ◽  
pp. 107-121
Author(s):  
R Dhaya
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Face Mask ◽  
World Health ◽  
Survey Study ◽  
Identification System ◽  
Two Stage ◽  
Public Places ◽  
The World ◽  
The Face

The World Health Organization (WHO) considers the COVID-19 Coronavirus to be a global pandemic. The most effective form of protection is to wear a face mask in public places. Moreover, the COVID-19 pandemic prompted all the countries to set up a lockdown to prevent viral transmission. According to a survey study, the use of facemasks at work decreases the chances of fast transmission. If the facemasks are not used or are worn incorrectly, it contributes to the third and fourth waves of the corona virus spreading throughout the world. This motivates us to conduct an efficient investigation of the face mask identification system and monitor people, who use suitable face mask in public places. Deep learning is the most effective approach for detecting whether or not a person is wearing a face mask in a crowded area. Using a multiclass deep learning technique, this research study proposes an efficient two stage identification (ETSI) for face mask detection. Whereas, the binary classification does not offer information about face mask detection and error. The proposed approach employs CNN's "ReLU" activation function to detect the face mask. Furthermore, in the current pandemic crisis, this research article offers a very efficient and precise approach for identifying COVID-19. Precision has increased as a result of the employment of a multi-class abbreviation in the final output.

scholarly journals Face with Mask Detection and Recognition for Smart Attendance System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1586-1591
Author(s):  
Shweta Panjabrao Dhawale
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Face Mask ◽  
Learning Framework ◽  
The Face ◽  
Face Detection And Recognition ◽  
Python Programming ◽  
Detection And Recognition

In this paper we will see the face mask detection and recognition for smart attendance system. In current pandemic situation our proposed system is very useful. We have used here face algorithm technique, python programming and to capture the images open cv is used., open cv2 now comes with a very new face recognizer class for the face recognition and popular computer vision liberaay started by intel in 1999. Open cv released under BSD licence that’s why used in the academic projects. We have used the concept of deep learning framework for the detection of faces. our aim is to present the study of previous attempts at face detection and recognition for smart attendance system by using deep learning .these is rapidly growing technology with its application in various aspects.

Detecting internet attacks using a neural network

2021 ◽  
Author(s):  
E.Yu. Silantieva ◽  
V.A. Zabelina ◽  
G.A. Savchenko ◽  
I.M. Chernenky
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Open Source Software ◽  
Network Traffic ◽  
Network Models ◽  
Detection Accuracy ◽  
Learning Models ◽  
Neural Network Models ◽  
Robust Identification ◽  
Variational Autoencoder

This study presents an analysis of autoencoder models for the problems of detecting anomalies in network traffic. Results of the training were assessed using open source software on the UNB ICS IDS 2017 dataset. As deep learning models, we considered standard and variational autoencoder, Deep SSAD approaches for a normal autoencoder (AE-SAD) and a variational autoencoder (VAE-SAD). The constructed deep learning models demonstrated different indicators of anomaly detection accuracy; the best result in terms of the AUC metric of 98% was achieved with VAE-SAD model. In the future, it is planned to continue the analysis of the characteristics of neural network models in cybersecurity problems. One of directions is to study the influence of structure of network traffic on the performance indicators of using deep learning models. Based on the results, it is planned to develop an approach of robust identification of security events based on deep learning methods.

scholarly journals An Automated Framework for Patient Identification and Verification Using Deep Learning

2020 ◽  
Vol 34 (6) ◽  
pp. 709-719
Author(s):  
Suresh Tommandru ◽  
Domnic Sandanam
Keyword(s):  
Deep Learning ◽  
Contrast Enhancement ◽  
Real Time ◽  
Detection System ◽  
Medical Emergency ◽  
Light Illumination ◽  
Learning Models ◽  
Patient Identification ◽  
Illumination Problem ◽  
The Face

Automated patient identification and verification are very important at a medical emergency and when patients are not carrying his/her identity. It is a risk factor that identifying the correct patient identity for doctors to provide medical treatment. The majority of the identification or verification is being done by wristbands, RFID tags, fingerprint, face detection by using handcraft feature-based face recognition systems. A new framework based on robust deep learning model and contrast enhancement is proposed in this paper. In the proposed work, the light illumination problem has been addressed by the contrast enhancement technique for deep learning models to recognize the face. It is proved that the inclusion of contrast enhancement is improving patient identification and verification. To evaluate the deep learning framework, the proposed deep learning models have been trained on our own dataset and have been tested with a real-time medical providing agency. The experimental results show that the proposed framework exhibits more robust test results with accuracy than existing hand-crafted techniques under the live webcam video capture for the real-time patient detection system.

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