Person recognition based on face and body information for domestic service robots

Abstract

Non-intrusive person recognition in different poses is a crucial task in the field of human-robot interaction (HRI). Most of the previous work for person recognition were based on face. In these approaches, if the person's face is invisible to the robot, it will ask the person to stand in a location suitable for the robot and look at the robot to recognize him. However in a normal interaction, the robot should be able to recognize a person without user cooperation to have acceptable interaction with users in real-time. In this paper, we address this problem using a combination of face and 3D information of a person's body. We used a mathematical framework based on the Bayesian decision theory for decision making. This combination can be used for recognizing scenarios where the pose of the people is unconstrained which makes it difficult to recognize a person by applying common face recognition algorithms. We tested our proposed approach in recognizing a five person group in various poses such as sitting and circular walking. The method was applied to a service robot equipped with the Kinect sensor. The results show a mean accuracy of 40.48% over all the poses using face alone, an accuracy of 67.23% using body alone, and an accuracy of 80.23% using the combination of the face and body information.