Leveraging the robot dialog state for visual focus of attention recognition

Abstract

The Visual Focus of Attention (what or whom a person is looking at) or VFOA is a fundamental cue in non-verbal communication and plays an important role when designing effective human-machine interaction systems. However, recognizing the VFOA of an interacting person is difficult for a robot, since due to low resolution imaging, eye gaze estimation is not possible. Rather, head pose cue is used as a substitute for gaze, but leads to ambiguities in its interpretation as VFOA indicator. In this paper, we investigate the use of the robot conversational state, which the robot is aware of, as contextual information to improve VFOA recognition from head pose. We propose a dynamic Bayesian model that accounts for the robot state (speaking status, person he addresses, reference to objects) along with a dynamic head-to-gaze mapping function. Experiments on a publicly available human-robot interaction dataset, where a humanoid robot plays the role of an art guide and quiz master, shows that using such conversational context is effective in improving VFOA.