Finger Contact Area Analysis with Convolutional Neural Networks

The detection of the contact area formed between a human finger and a counter surface is of great interest because it is the key parameter for various interaction parameters. Adhesional friction forces and the thermal contact conductance critically depend on the contact area, further influencing the tactile sensation of stickiness and warmth. The contact area is also of concern regarding safety issues. Injuries caused by objects slipping out of our hands might be prevented by optimizing the contact area and the concomitant grip through appropriate surface structures and material choice. Until now the contact area is mainly studied on smooth and transparent materials. The contact area is recorded optically and rule-based image processing methods can be used for detection. These methods might be insufficient for rough surfaces where the contact area is optically unclear due to light scattering. In this paper we demonstrate the successful analysis of such optically unclear contact area images via convolutional neural networks to identify the fingerprint ridges in contact with structured surfaces. The proposed method relies on the generation of synthetic contact images that provide the pixelwise ground truth for the efficient training of a segmentation pipeline based on convolutional neural networks.