(a) The Generalized Perceived Input Point Model: a user has repeatedly acquired the shown crosshairs using finger postures ranging from 90° (vertical) to 15° pitch (almost horizontal). The five white ovals each contain 65% of the resulting contact points. The key observation is that the ovals are offset with respect to each other, yet small. We find a similar effect across different levels of finger roll and finger yaw, and across users. We conclude that the inaccuracy of touch (dashed oval) is primarily the result of failure to distinguish between different users and finger postures, rather than the fat finger problem.
(b) The ridges of this fingerprint belong to the front region of a fingertip. Our RidgePad prototype uses this observation to deduce finger posture and user ID during each touch. This allows it to exploit the new model and obtain 1.8 times higher accuracy than capacitive sensing.