Learning Gestures for Visually Mediated Interaction

This paper reports initial research on supporting Visually Mediated Interaction (VMI) by developing person-specific and generic gesture models for the control of active cameras. We describe a time-delay variant of the Radial Basis Function (TDRBF) network and evaluate its performance on recognising simple pointing and waving hand gestures in image sequences. Experimental results are presented that show that high levels of performance can be obtained for this type of gesture recognition using these techniques, both for particular individuals and across a set of individuals. Characteristic visual evidence can be automatically selected and used even to recognise individuals from their gestures, depending on the task demands.