Fingernail image registration using Active Appearance Models

Abstract

This paper demonstrates the effectiveness of registering fingernail images using Active Appearance Models (AAM). A shape model is formed using two contours, one to trace the edge of the fingernail and a second to outline the edge of the finger. This model is used to create an AAM, which is then used to register several hundred images. Using the images and associated force measurements, three different models relating pixel intensity to finger contact force are formed. This process is repeated for eight subjects. These image-force models could then be used to measure the finger contact force without obstructing the haptic sense or instrumenting the object being touched, allowing for more natural interaction between human and robot. Two of the models are found to predict force with an RMS error of 0.3N normal force (4% of the full range) and 0.23N shear force (3% of the full range). These two models are also shown to be nearly independent of image resolution, with no significant difference in the results for resolutions between 10-by-10 and 50-by-50. The simpler of the two models relies on the Texture Parameters generated by the AAM. Since these parameters are already calculated as part of the registration process, this removes an additional calculation step and could lead to improved real-time processing.