Automatic Features Identification with Infrared Thermography in Fever Screening

Abstract

The goal of this thesis is to develop an algorithm to process infrared images and achieve automatic identification of moving subjects with fever. The identification is based on two main features: the distinction between the geometry of a human face and other objects in the field of view of the camera, and the temperature of the radiating object. Infrared thermography is a remote sensing technique used to measure temperatures based on emitted infrared radiation. Applications include fever screening in major public places such as airports and hospitals. Current accepted practice of screening requires people to stay in a line and temperature measurements are carried out for one person at a time. However in the case of mass screening of moving people the accuracy of the measurements is still under investigation. An algorithm constituting of image processing to threshold objects based on the temperature, template matching and hypothesis testing is proposed to achieve automatic identification of fever subjects. The algorithm was first tested on training data to obtain a threshold value (used to discriminate between face and non face shapes) corresponding to a false detection rate of 5%, which in turn corresponds to 85% probability of detection using Neyman-Pearson criterion. By testing the algorithm on several simulated and experimental images (which reflect relevant scenarios characterizing crowded places) it is observed that it can be beneficially implemented to introduce automation in the process of detecting moving subjects with fever.