![]() ![]() Findings – The system is capable of recognizing static gestures comprised of the face and hand poses, and dynamic gestures of face in motion. Then, face and hand poses are segmented from the camera frame buffer using the person's specific skin color information and classified by the subspace method. First, the system identifies the user using the eigenface method. In this knowledge model, necessary frames are defined for the known users, robots, poses, gestures and robot behaviors. Design/methodology/approach – A frame-based knowledge model is defined for the gesture interpretation and HRI. This paper aims to describe a gesture-based human-robot interaction (HRI) system using a knowledge-based software platform. Purpose – Achieving natural interactions by means of vision and speech between humans and robots is one of the major goals that many researchers are working on. ![]()
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