Automatic Material Classification Using Thermal Finger Impression

Natural surfaces offer the opportunity to provide augmented reality interactions in everyday environments without the use of cumbersome body-mounted equipment. One of the key techniques of detecting user interactions with natural surfaces is the use of thermal imaging that captures the transmitted body heat onto the surface. A major challenge of these systems is detecting user swipe pressure on different material surfaces with high accuracy. This is because the amount of transferred heat from the user body to a natural surface depends on the thermal property of the material. If the surface material type is known, these systems can use a material-specific pressure classifier to improve the detection accuracy. In this work, we address to solve this problem as we propose a novel approach that can detect material type from a user’s thermal finger impression on a surface. Our technique requires the user to touch a surface with a finger for 2 s. The recorded heat dissipation time series of the thermal finger impression is then analyzed in a classification framework for material identification. We studied the interaction of 15 users on 7 different material types, and our algorithm is able to achieve 74.65% material classification accuracy on the test data in a user-independent manner.