Spatial normalization of facial thermal images using facial landmarks

Human–computer interaction (HCI) is an interaction for mutual communication between humans and computers. HCI needs to recognize the human state quantitatively and in real-time. Although it is possible to quantitatively evaluate the human condition by measuring biological signals, the challenge is that it often requires physical constraints. There is an increasing interest in a non-contact method of estimating physiological and psychological states by measuring facial skin temperature using infrared thermography. However, due to individual differences in face shape, the accuracy of physiological and psychological state estimation using facial thermal images was sometimes low. To solve this problem, we hypothesized that spatial normalization of facial thermal image (SN-FTI) could reduce the effect of individual differences in facial shape. The objective of this study is to develop a method for SN-FTI and to evaluate the effect of SN-FTI on the estimation of physiological and psychological states. First, we attempted spatial normalization using facial features. The results suggested that SN-FTI would result in the same face shape among individuals. Since there are individual differences in facial skin temperature distribution, the inter-individual correlation coefficient is suggested to be lower than the intra-individual correlation coefficient. Next, we modeled the estimated drowsiness level using SN-FTIs and compared it with Normal. The results showed that SN-FTI slightly improved the discrimination rate of drowsiness level. SN-FTIs were suggested to reduce the effect of individual differences in facial structure on the estimation of physiological and psychological states.