Designing and Evaluating a Social Gaze-Control System for a Humanoid Robot

• Published in: IEEE transactions on human-machine systems 2014-04, Vol.44 (2), p.157-168
• Main Authors: Zaraki, Abolfazl, Mazzei, Daniele, Giuliani, Manuel, De Rossi, Danilo
• Format: Article
• Language: English
• Subjects: Active vision; Applied sciences; Artificial intelligence; Biological and medical sciences; Computer science; control theory; systems; context-dependent social gaze behavior; Control systems; Control theory. Systems; Exact sciences and technology; Face; Face recognition; Feature extraction; Fundamental and applied biological sciences. Psychology; Human; human-robot interaction; Humanoid; Humanoid robots; Pattern recognition. Digital image processing. Computational geometry; Pilots; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Robot kinematics; Robotics; Robots; Running; scene analysis; social attention; Social interactions. Communication. Group processes; Social psychology; Speech and sound recognition and synthesis. Linguistics; Speech recognition; Tracking; Visualization
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2014.2303083

This paper describes a context-dependent social gaze-control system implemented as part of a humanoid social robot. The system enables the robot to direct its gaze at multiple humans who are interacting with each other and with the robot. The attention mechanism of the gaze-control system is based on features that have been proven to guide human attention: nonverbal and verbal cues, proxemics, the visual field of view, and the habituation effect. Our gaze-control system uses Kinect skeleton tracking together with speech recognition and SHORE-based facial expression recognition to implement the same features. As part of a pilot evaluation, we collected the gaze behavior of 11 participants in an eye-tracking study. We showed participants videos of two-person interactions and tracked their gaze behavior. A comparison of the human gaze behavior with the behavior of our gaze-control system running on the same videos shows that it replicated human gaze behavior 89% of the time.