Face with Mask Detection in Thermal Images Using Deep Neural Networks

Kowalczyk, Natalia; Rumiński, Jacek

doi:10.3390/s21196387

Cited by 13 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is because certain experiments allow their subjects to freely move their head and body in any direction as long as the subject's head positions can still be captured by a thermal camera. The global facial tracking method is noticeable in the studies proposed in [2], [18], with the use the DL-based object tracking method to track the entire face image with or without wearing a protective face mask with nearly 99% correctly as compared to local facial tracking that may use nose area in studies described in [3], [32]- [34]. It differs from previous studies in [35], which deliberately included the nose and mouth regions because the airflow mainly gets in and out of those regions.…”

Section: ) Facial Tracking Methodsmentioning

confidence: 99%

“…Recent studies have shown insufficient evidence to support the implementation of extractor methods to solve the problems with subjects wearing glasses or protective face masks during the experiments. This could explain why several researchers have purposefully included their subjects wearing glasses or protective face masks to evaluate the preciseness of their purposed methods [18], [19].…”

Section: A Data Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Facial Thermography Assessment for Vital Signs Estimation

2022

View full text Add to dashboard Cite

Estimated vital signs might include a variety of measurements that can be used in detecting any abnormal conditions by analyzing facial images from continuous monitoring with a thermal video camera. To overcome the limitless human visual perceptions, thermal infrared has proven to be the most effective technique for visualizing facial colour changes that could have been reflected by changes in oxygenation levels and blood volume in facial arteries. This study investigated the possibility of vital signs estimation using physiological function images converted from the thermal infrared images in the same ways that visible images are used, with a need for an efficient extractor method as correction procedures that have used datasets that include images with and without wearing glasses or protective face masks. This paper, summarize thermal images using advanced machine learning and deep learning methods with satisfactory performance. Also, we presented the evaluation matrices that were included in the assessment based on statistical analysis, accuracy measures and error measures. Finally, to discuss future gaps and directions for further evaluations.INDEX TERMS Thermal images, features extractions, vital signs estimation, evaluation matrices.

show abstract

Section: ) Facial Tracking Methodsmentioning

confidence: 99%

Section: A Data Preprocessingmentioning

confidence: 99%

A Review of Facial Thermography Assessment for Vital Signs Estimation

2022

View full text Add to dashboard Cite

show abstract

“…In [196] Another notable group of works [29,197,198] focused on the collection of datasets for COVID-19 related applications using thermal imaging. Queiroz et al [29], for example, utilized a large-scale multimodal dataset known as SpeakingFaces [208] that consists of thermal images as well as visual and audio streams.…”

Section: Datasetsmentioning

confidence: 99%

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

Eyiokur¹,

Kantarcı²,

Erakin³

et al. 2022

Preprint

View full text Add to dashboard Cite

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting facehand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field.Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

show abstract

“…There have been several previous studies regarding thermal camera sensors and LoRa. Thermal camera sensors are used for tracking and facial recognition because there is abuse in facial manipulation by using excessive makeup and using masks that are not following the rules [22] [23]. In addition, a thermal camera sensor is used to find out the whereabouts of a person somewhere [24].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of LoRa-Based Forest Fire Monitoring System

Apriani

Oktaviani

Sofian

2022

Journal of Robotics and Control

View full text Add to dashboard Cite

One of the great disasters on earth is forest fires. Attempts to detect disaster events have been made with the help of monitoring technology. However, the problem is that the sensor is less responsive to detecting the presence of fire. Furthermore, sending information about fire incidents throughout the forest cannot use the existing communication platform. Therefore, we designed a forest fire monitoring system using LoRa. This technology is based on wireless which can transmit data across the forest. To detect the presence of fire, Arduino Uno is used as a microcontroller that regulates input from the AMG8833 sensor and GPS Ubox 6M. The experiment shows that the AMG8833 sensor is more sensitive in detecting the presence of fire as the catch range changes between 3 to 10 meters. In that distance range, hotspots were detected 19.25 oC to 122.5 oC when testing the sensor node is done. The monitoring system developed in this study demonstrated that sensor nodes and gateways could communicate up to 500 meters apart with a signal quality of -134 dBm. The best LoRa configuration mode for this communication capability is a Bandwidth of 250, a Code Rate of 4/5, and a Spread Factor of 10.One of the great disasters on earth is forest fires. Attempts to detect disaster events have been made with the help of monitoring technology. However, the problem is that the sensor is less responsive to detecting the presence of fire. Furthermore, sending information about fire incidents throughout the forest cannot use the existing communication platform. Therefore, we designed a forest fire monitoring system using LoRa. This technology is based on wireless which can transmit data across the forest. To detect the presence of fire, Arduino Uno is used as a microcontroller that regulates input from the AMG8833 sensor and GPS Ubox 6M. The experiment shows that the AMG8833 sensor is more sensitive in detecting the presence of fire as the catch range changes between 3 to 10 meters. In that distance range, hotspots were detected 19.25 oC to 122.5 oC when testing the sensor node is done. The monitoring system developed in this study demonstrated that sensor nodes and gateways could communicate up to 500 meters apart with a signal quality of -134 dBm. The best LoRa configuration mode for this communication capability is a Bandwidth of 250, a Code Rate of 4/5, and a Spread Factor of 10.

show abstract

Face with Mask Detection in Thermal Images Using Deep Neural Networks

Cited by 13 publications

References 29 publications

A Review of Facial Thermography Assessment for Vital Signs Estimation

A Review of Facial Thermography Assessment for Vital Signs Estimation

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

Design and Implementation of LoRa-Based Forest Fire Monitoring System

Contact Info

Product

Resources

About