See me, see you: a lightweight method for discriminating user touches on tabletop displays

  • Authors:

  • Hong Zhang;Xing-Dong Yang;Barrett Ens;Hai-Ning Liang;Pierre Boulanger;Pourang Irani

  • Affiliations:

  • University of Manitoba, Winnipeg, Manitoba, Canada;University of Alberta, Edmonton, Alberta, Canada;University of Manitoba, Winnipeg, Manitoba, Canada;University of Manitoba, Winnipeg, Manitoba, Canada;University of Alberta, Edmonton, Alberta, Canada;University of Manitoba, Winnipeg, Manitoba, Canada

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2012

Quantified Score

Hi-index 0.01

Visualization

Abstract

Tabletop systems provide a versatile space for collaboration, yet, in many cases, are limited by the inability to differentiate the interactions of simultaneous users. We present See Me, See You, a lightweight approach for discriminating user touches on a vision-based tabletop. We contribute a valuable characterization of finger orientation distributions of tabletop users. We exploit this biometric trait with a machine learning approach to allow the system to predict the correct position of users as they touch the surface. We achieve accuracies as high as 98% in simple situations and above 92% in more challenging conditions, such as two-handed tasks. We show high acceptance from users, who can self-correct prediction errors without significant costs. See Me, See You is a viable solution for providing simple yet effective support for multi-user application features on tabletops.