See me, see you
Hong Zhang, Xing-Dong Yang, Barrett M. Ens, Hai‐Ning Liang, Pierre Boulanger, Pourang Irani · 2012
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.