Blind signal processing of facial thermal images based on independent component analysis

Abstract

There have been a number of investigations into image recognition and the assessment of human physiological states using infrared thermography. Assessing a human's physiological state by infrared thermography typically exploits the skin temperature of the nasal region and forehead, whereas other parts of the face are less frequently used. The present study has developed a method of analyzing facial thermal images (FTIs) by independent component analysis (ICA), a type of blind signal processing (BSP). ICA is a well-known statistical analysis tool that estimates the original source signal from observed mixture signals. When applied to thermal images, ICA is predicted to extract blind signals such as those from other parts of the face. In this study, the authors use ICA to conduct BSP on a series of FTIs. The extracted independent components are shown to represent temperature fluctuations from the opening and closing of the eyes, respiration, truncal sites such as the cheeks and forehead, and possibility of sympathetic nervous system activity. The FTIs reconstructed after the removal of artifacts indicate the local features that the blind signal cannot extract from the original FTIs.