Vision-based interfaces, such as those made popular by the Microsoft Kinect, suffer from the Midas Touch problem: every user motion can be interpreted as an interaction. In response, we developed an algorithm that combines facial features, body pose and motion to approximate a user's intention to interact with the system. We show how this can be used to determine when to pay attention to a user's actions and when to ignore them. To demonstrate the value of our approach, we present results from a 30-person lab study conducted to compare four engagement algorithms in single and multi-user scenarios. We found that combining intention to interact with a 'raise an open hand in front of you' gesture yielded the best results. The latter approach offers a 12% improvement in accuracy and a 20% reduction in time to engage over a baseline 'wave to engage' gesture currently used on the Xbox 360.
['Julia Schwarz', 'Charles Claudius Marais', 'Tommer Leyvand', 'Scott E. Hudson', 'Jennifer Mankoff']
Combining body pose, gaze, and gesture to determine intention to interact in vision-based interfaces