See Me, See You: A Lightweight Method for Discriminating User Touches on Tabletop Displays

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.

Video

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Citation

Hong Zhang, Xing-Dong Yang, Barrett Ens, Hai-Ning Liang, Pierre Boulanger and Pourang Irani. 2012. See Me, See You: A Lightweight Method for Discriminating User Touches on Tabletop Displays. In Proceedings of the 30th international conference on Human factors in computing systems (CHI 2012). Austin, Texas. ACM, 2327-2336.

Bibtext Entry

@INPROCEEDINGS { hongCHI2012,
    AUTHOR = { Hong Zhang and Xing-Dong Yang and Barrett Ens and Hai-Ning Liang and Pierre Boulanger and Pourang Irani },
    TITLE = { See Me, See You: A Lightweight Method for Discriminating User Touches on Tabletop Displays },
    BOOKTITLE = { Proceedings of the 30th international conference on Human factors in computing systems (CHI 2012) },
    YEAR = { 2012 },
    PAGES = { 2327--2336 },
    PUBLISHER = { ACM },
    ADDRESS = { Austin, Texas },
}