Detecting $\sim$10 mK Face Temperature Change Based on Lock-in Thermography Referencing Heartbeat

Infrared thermography, which has widely spread particularly during the COVID-19 period, has been effectively used for research on health monitoring and emotion estimation. Nevertheless, detecting minute temperature changes with thermography is challenging as it is disturbed by not only noise but also outside temperature surrounding the object. In this study, we demonstrate detecting face temperature variation by implementing lock-in thermography using heartbeat signals as a reference. It allows us to detect minute temperature changes, as low as $\sim$10 mK, on the forehead with a commercially available thermal camera. The proposed approach enables stable measurement of body temperature variation, showing potential for non-contact emotion estimation.

Point Anywhere: Directed Object Estimation from Omnidirectional Images

One of the intuitive instruction methods in robot navigation is a pointing gesture. In this study, we propose a method using an omnidirectional camera to eliminate the user/object position constraint and the left/right constraint of the pointing arm. Although the accuracy of skeleton and object detection is low due to the high distortion of equirectangular images, the proposed method enables highly accurate estimation by repeatedly extracting regions of interest from the equirectangular image and projecting them onto perspective images. Furthermore, we found that training the likelihood of the target object in machine learning further improves the estimation accuracy.