High-precision thermography based on JMAP inference for human face temperature

Chu, Ning; Yao, Zhong; Hou, Yaochun

doi:10.1117/12.2574940

Cited by 2 publications

(1 citation statement)

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“…Moreover, they proposed optimized methods for estimating body temperature, demonstrating that IRI devices’ performance could be affected by many factors (e.g., location and size of the target plane and blackbody uncertainty) [ 33 ]. In order to limit the uncertainty of IRI measurements related to human skin radiation model, Chu et al [ 34 ] proposed the joint maximum a posterior (JMAP) approach with a hierarchical prior model of the thermal radiation to be applied on human face, to provide an efficient and accurate evaluation of abnormal T sk . To limit the effects due to inhomogeneities of the measurement conditions, operational guidelines for identifying a febrile human using a screening thermograph (ISO/TR 13154:2009 ISO/TR 80600) have been defined.…”

Section: Methodologies For Iri-based Fever Detectionmentioning

confidence: 99%

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

Perpetuini

Filippini

Cardone

et al. 2021

IJERPH

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Infrared thermal imaging (IRI) is a contact-less technology able to monitor human skin temperature for biomedical applications and in real-life contexts. Its capacity to detect fever was exploited for mass screening during past epidemic emergencies as well as for the current COVID-19 pandemic. However, the only assessment of fever may not be selective for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Hence, novel approaches for IRI data analysis have been investigated. The present review aims to describe how IRI have been employed during the last epidemics, highlighting the potentialities and the limitations of this technology to contain the contagions. Specifically, the methods employed for automatic face recognition and fever assessment and IRI’s performances in mass screening at airports and hospitals are reviewed. Moreover, an overview of novel machine learning methods for IRI data analysis, aimed to identify respiratory diseases, is provided. In addition, IRI-based smart technologies developed to support the healthcare during the COVID-19 pandemic are described. Finally, relevant guidelines to fully exploit IRI for COVID-19 identification are defined, to improve the effectiveness of IRI in the detection of the SARS-CoV-2 infection.

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Section: Methodologies For Iri-based Fever Detectionmentioning

confidence: 99%