Development of abnormal facial temperature detection technology using thermal imaging to prevent the spread of infectious diseases

Abstract

Substantial research has been conducted on image-based augmentation, segmentation, detection, and classification of COVID-19. Thermal image classification methods have been used to identify people with high fever. However, these methods cannot always identify infected patients because aberrant images cannot be detected using face temperatures. For example, if the individual in the thermal image cleansed his/her face a few minutes earlier or was perspiring, the method cannot reliably determine their facial temperature. Moreover, the active movements of the human body can produce temperature variations in the face. Thus, achieving high accuracy remains challenging in thermal facial image identification. Therefore, in this study, a novel research has been conducted to solve the problems. Based on preliminary experimental results, a deep-learning-based classification system was proposed for detecting faces with abnormal facial temperatures in thermal pictures. Two different experiments including abnormal face images detection and classification were conducted. To conduct these experiments, a self-collected thermal face-image dataset including abnormal (faces after exercise, sweaty and wet faces) and normal images was used. In the detection method, a binary image classification was conducted to classify the abnormal and normal images. Moreover, in the classification method, an input image is classified into four classes, such as normal faces (class 1) and three abnormal faces, namely, after physical activity (class 2), sweaty face (class 3), and wet face (class 4). Testing on the dataset collected in this study revealed that the proposed detection and classification methods achieved F1-score of 95.71 and 96.17, respectively. These performances are superior to those of the state-of-the-art methods.