The Generalized Perceived Input Point Model and How to Double Touch Accuracy by Extracting Fingerprints

with Patrick Baudisch

Figure 1

Figure 1

(click for highres image)

(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.

Abstract

It is generally assumed that touch input cannot be accurate because of the fat finger problem, i.e., the softness of the fingertip combined with the occlusion of the target by the finger. In this paper, we show that this is not the case. We base our argument on a new model of touch inaccuracy. Our model is not based on the fat finger problem, but on the perceived input point model. In its published form, this model states that touch screens report touch location at an offset from the intended target. We generalize this model so that it represents offsets for individual finger postures and users. We thereby switch from the traditional 2D model of touch to a model that considers touch a phenomenon in 3-space. We report a user study, in which the generalized model explained 67% of the touch inaccuracy that was previously attributed to the fat finger problem.

In the second half of this paper, we present two devices that exploit the new model in order to improve touch accuracy. Both model touch on per-posture and per-user basis in order to increase accuracy by applying respective offsets. Our RidgePad prototype extracts posture and user ID from the user’s fingerprint during each touch interaction. In a user study, it achieved 1.8 times higher accuracy than a simulated capacitive baseline condition. A prototype based on optical tracking achieved even 3.3 times higher accuracy. The increase in accuracy can be used to make touch interfaces more reliable, to pack up to 3.3^2 > 10 times more controls into the same surface, or to bring touch input to very small mobile devices.

RidgePad

RidgePad

RidgePad derives finger posture and user ID from each touch event and thereby obtains 1.8 times higher accuracy than capacitive sensing. RidgePad is based on an L SCAN Guardian fingerprint scanner.

Publication

BibTeX

@inproceedings{holz2010,
	author = {Holz, Christian and Baudisch, Patrick},
	title = {The generalized perceived input point model and how to double touch accuracy by extracting fingerprints},
	booktitle = {CHI '10: Proceedings of the 28th international conference on Human factors in computing systems},
	year = {2010},
	isbn = {978-1-60558-929-9},
	pages = {581--590},
	location = {Atlanta, Georgia, USA},
	doi = {http://doi.acm.org/10.1145/1753326.1753413},
	publisher = {ACM},
	address = {New York, NY, USA},
}

project page at Hasso Plattner Institute/HCI group