Remote Mass Facial Temperature Screening in Varying Ambient Temperatures and Distances

Abstract

Remote body temperature measurement using infrared thermography has been widely deployed worldwide to detect feverish persons, but the measurement accuracy is affected by various factors including ambient temperature and sensor-subject distance. We present a novel compensation model to address the undesirable interacting influence of ambient temperature and sensor-subject distance during remote facial temperature screening in real-world setting. We derived our model on site-data collected over 12 months and demonstrated the significant linear relationship between ambient temperature and the measured temperature from a thermal camera. In addition, the interaction between the effects of sensor-subject distance and ambient temperature on the measured temperature is significant. Our model can significantly reduce the measurement error (MAE) by 23.5% and is better than the best existing models. The model can also extend the detection distance by up to 46% with sensitivity and specificity over 90%.