ABSTRACT
Infrared thermographs (IRTs, also called thermal cameras) have been used to remotely measure elevated body temperature (BT) and respiratory rate (RR) during infectious disease outbreaks, such as COVID-19. To facilitate the fast measurement of BT and RR using IRTs in densely populated venues, it is desirable to have IRT algorithms that can automatically identify the best facial locations in thermal images to extract these vital signs. The IEC 80601-2-59:2017 standard suggests that the regions medially adjacent to the inner canthi of the eyes are robust BT measurement sites. The nostril regions, on the other hand, are often used for RR estimation. However, it is more difficult to automatically identify inner canthi and nostrils in thermal images than in visible-light images, which are rich with exploitable features. In this paper, a unique system that can detect inner canthi and outer nostril edges directly in thermal images in two phases is introduced. In Phase I, original thermal images were processed in four different ways to enhance facial features to facilitate inner canthus and nostril detection. In Phase II, landmarks of the inner canthi and outer nostril edges were detected in two steps: (1) face detection using the Single Shot Multibox Detector (SSD) and (2) facial landmark detection to locate the inner canthi and outer nostril edges. The face detection, facial landmark detection, and overall system accuracies were evaluated using the intersection over union, normalized Euclidean distance, and success detection rate metrics on a set of 36 thermal images collected from 12 subjects using three different IRTs. Additional validation was performed on a subset of 40 random thermal images from the publicly available Tufts Face Database. The results revealed that the processed images - referred to as ICLIP images - yielded the highest landmark localization accuracy from the four types of processed thermal images, verifying that the system can automatically and accurately estimate the inner canthus and nostril locations in thermal images. The proposed system can be applied in IRT algorithms to provide reliable temperature measurements and RR estimates during infectious disease outbreaks. © 2013 IEEE.
ABSTRACT
Infrared thermographs (IRTs) implemented according to standardized best practices have shown strong potential for detecting elevated body temperatures (EBT), which may be useful in clinical settings and during infectious disease epidemics. However, optimal IRT calibration methods have not been established and the clinical performance of these devices relative to the more common non-contact infrared thermometers (NCITs) remains unclear. In addition to confirming the findings of our preliminary analysis of clinical study results, the primary intent of this study was to compare methods for IRT calibration and identify best practices for assessing the performance of IRTs intended to detect EBT. A key secondary aim was to compare IRT clinical accuracy to that of NCITs. We performed a clinical thermographic imaging study of more than 1000 subjects, acquiring temperature data from several facial locations that, along with reference oral temperatures, were used to calibrate two IRT systems based on seven different regression methods. Oral temperatures imputed from facial data were used to evaluate IRT clinical accuracy based on metrics such as clinical bias (Δcb), repeatability, root-mean-square difference, and sensitivity/specificity. We proposed several calibration approaches designed to account for the non-uniform data density across the temperature range and a constant offset approach tended to show better ability to detect EBT. As in our prior study, inner canthi or full-face maximum temperatures provided the highest clinical accuracy. With an optimal calibration approach, these methods achieved a Δcb between ±0.03 °C with standard deviation (σΔcb) less than 0.3 °C, and sensitivity/specificity between 84% and 94%. Results of forehead-center measurements with NCITs or IRTs indicated reduced performance. An analysis of the complete clinical data set confirms the essential findings of our preliminary evaluation, with minor differences. Our findings provide novel insights into methods and metrics for the clinical accuracy assessment of IRTs. Furthermore, our results indicate that calibration approaches providing the highest clinical accuracy in the 37-38.5 °C range may be most effective for measuring EBT. While device performance depends on many factors, IRTs can provide superior performance to NCITs.