**1. Introduction**

Coronaviruses (CoVs) are viruses that cause respiratory infections in animals such as birds and mammals, including humans. There have been seven recorded CoVs that have caused serious harm to human health, with two of them responsible for the epidemics that emerged in Hong Kong in 2003 and Saudi Arabia in 2012 [1]. In December 2019, a new CoV called SARS-CoV-2 (the virus that causes the disease COVID-19) emerged in the city of Wuhan, China. In the first part of 2020, this virus spread to virtually every country in the

de Almeida, G.M.; Cuadros, M.A.d.S.L.; Campos, H.L.M.; Nunes, R.B.; Simão, J.; Muniz, P.R. Recognition of Human Face Regions under Adverse Conditions—Face Masks and Glasses—In Thermographic Sanitary Barriers through Learning Transfer from an Object Detector. *Machines* **2022**, *10*, 43. https://doi.org/10.3390/ machines10010043

**Citation:** da Silva, J.R.;

Academic Editors: Marcos de Sales Guerra Tsuzuki, Marcosiris Amorim de Oliveira Pessoa and Alexandre Acássio

Received: 23 November 2021 Accepted: 24 December 2021 Published: 7 January 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

world. On 30 January 2020, the World Health Organization (WHO) declared the COVID-19 pandemic an international emergency [1].

As the transmission rate increased, many studies developing detection and diagnostic technologies for people infected with COVID-19 have emerged. Mild cases of COVID-19 present with symptoms such as cough, fever, runny nose, sore throat, and difficulty breathing, whereas severe cases can cause pneumonia [2]. As fever is one of the most recurrent symptoms of the infection, some countries perform temperature measurements on people at airports, bus and train stations, company gates, and other shared and public spaces. These actions sought to detect symptomatic patients to contain the spread of the virus [3–5].

Currently, there are several ways to measure temperatures using devices or infrared thermometers. However, these methods do not guarantee measurement reliability, as incorrect use may lead to measurements of a person or an object with significant errors. Furthermore, as it is a manual process, the collection and recording of temperatures are subject to human error in addition to intrinsic measurement uncertainties, causing many screening errors in barriers currently in use [6–8].

In health, noninvasive measuring and intervention equipment can prevent germ spread and contribute to the practicality of the diagnosis process [9]. Furthermore, the high demand for fever measuring devices has caused many companies to enter the field without understanding the mechanism of human temperature measurement, employing technology that may incorrectly measure body temperature [10].

Thermal imaging cameras, or thermal imagers, utilize infrared thermography to measure temperature; this is a noninvasive, fast, and objective technique. All objects with temperatures above 0 K emit infrared radiation, and the amount of radiation emitted increases with temperature. Therefore, thermography can measure the surface temperature of the bodies [11]. Thus, it is possible to develop innovative technologies and automate the measurement processes by applying computer vision algorithms to thermographic images [12].

Computer vision, a subfield of artificial intelligence, can help provide solutions to many complex problems in health; it can also assist in the diagnosis and spread prevention of COVID-19 [13]. Additionally, Ulhaq et al. [13] presented computer vision techniques to control the virus spread, including infrared thermography processing. In this case, the algorithms detect regions of interest (ROIs) extracted from the original image.

In [14], the authors applied a series of machine learning (ML) algorithms to different tasks related to processing thermographic facial images. This study also presents a method to estimate head position to increase the ability to detect reference points in nonfrontal faces. These techniques are essential for improving the accuracy of the detection algorithm by capturing the face at a more appropriate angle. However, for Wang et al. [9], the facial temperature measurement should not be used only in a small area and ignore other sectors because the facial thermal image can show specific thermal characteristics among different regions.

Thermographic cameras also have significant potential for use in measuring the temperature of the human body surface, i.e., skin temperature [15]. However, the literature indicates inconsistent diagnostic performance, possibly due to wide variations in the implemented methodologies. This study evaluated the effectiveness of fever diagnosis and the effect of measuring temperatures in 17 facial regions; it contributes to the elucidation of the impact that location has on facial temperature measurement and other issues regarding the performance of febrility detection methods.

Propelled by the lack of published studies and the urgent need for sanitary barriers for screening people who may have COVID-19, this study aims to develop a solution for screening without physical contact or holding people and with only minimal interference of the flow of the site where the barrier is established. This work was supported by the Government of the State of Espírito Santo through the Foundation for Support to Research and Innovation of Espírito Santo (FAPES).

This paper presents an intelligent system using the Transfer Learning technique of a Deep Learning network trained to detect objects. The algorithm analyzes thermographic images to quickly detect the subject's face, forehead, eyes, and ears, as these areas present the highest temperatures in the frontal and lateral regions of the head. The algorithm can then analyze the temperature of the ROI and, subsequently, estimate body surface temperature more accurately and efficiently than manual measurement methods. Additionally, the algorithm can incorporate suitable diagnostic criteria for the different ROIs with different febrility thresholds.

This article is an expanded article from a conference paper presented at the 14th IEEE/IAS International Conference on Industry Applications (Induscon) [16], whose theme was 'Innovation in the time of COVID-19 . This version introduces more details on human infrared thermography, presents more tests with volunteers, and applies Optical Character Recognition (OCR) technology to identify maximum and minimum temperatures in thermographs. These add-ons improved the work previously carried out in the automatic detection of febrile people at sanitary barriers, which is very relevant in this phase of the COVID-19 pandemic, where new variants of the virus are emerging.
