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Carpus: a non-intrusive user identification technique for interactive surfaces

Published: 07 October 2012 Publication History

Abstract

Interactive surfaces have great potential for co-located collaboration because of their ability to track multiple inputs simultaneously. However, the multi-user experience on these devices could be enriched significantly if touch points could be associated with a particular user. Existing approaches to user identification are intrusive, require users to stay in a fixed position, or suffer from poor accuracy. We present a non-intrusive, high-accuracy technique for mapping touches to their corresponding user in a collaborative environment. By mounting a high-resolution camera above the interactive surface, we are able to identify touches reliably without any extra instrumentation, and users are able to move around the surface at will. Our technique, which leverages the back of users' hands as identifiers, supports walk-up-and-use situations in which multiple people interact on a shared surface.

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      cover image ACM Conferences
      UIST '12: Proceedings of the 25th annual ACM symposium on User interface software and technology
      October 2012
      608 pages
      ISBN:9781450315807
      DOI:10.1145/2380116
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 07 October 2012

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      Author Tags

      1. interactive tabletops
      2. multi-touch interaction
      3. multi-user applications
      4. surface computing
      5. user identification

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      Overall Acceptance Rate 561 of 2,567 submissions, 22%

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