A Facial Expression Imitation System for the Primitive of Intuitive Human-Robot Interaction

摘要

This Chapter has attempted to deal with the issues on establishing a facial expression imitation system for natural and intuitive interactions with humans. Several real-time cognition abilities are implemented to a robotic system such as face detection, face tracking, and facial expression recognition. Moreover, a robotic system with facial components is developed, which is able to imitate human's facial expressions. A method of recognizing facial expressions is proposed through the use of an innovative rectangle feature. Using the AdaBoost algorithm, an expanded version of Viola and Jones' method has been suggested as a new approach. We deal with 7 facial expressions: neutral, happiness, anger, sadness, surprise, disgust, and fear. For each facial expression, we found five suitable rectangle features using the AdaBoost learning algorithm. These 35 rectangle features and 7 rectangle features were used to find new weak classifiers for facial expression recognition. A real-time performance rate can be achieved through constructing the strong classifier while extracting a few efficient weak classifiers by AdaBoost learning. In addition, an active vision system for social interaction with humans is developed. We proposed a high-speed bell-shaped velocity profiler to reduce the magnitude of jerking motion and used this method to control 12 actuators in real-time. We proved our distributed control structure and the proposed fast bell-shaped velocity profiler to be practical. Several basic algorithms, face detection and tracking, are implemented on the developed system. By directing the robot's gaze to the visual target, the person interacting with the robot can accurately use the robot's gaze as an indicator of what the robot is attending to. This greatly facilitates the interpretation and readability of the robot's behavior, as the robot reacts specifically to the thing that it is looking at. In order to implement visual attention, the basic functionality mentioned above, e.g. face detection, tracking and motor control, is needed. Finally, we introduced an artificial facial expression imitation system using a robot head. There are a number of real-time issues for developing the robotic system. In this Chapter, one solution for developing it is addressed. Our final goal of this research is that humans can easily perceive motor actions semantically and intuitively, regardless of what the robot intends. However, our research lacks a sound understanding of natural and intuitive social interactions among humans. Our future research will focus on perceiving the mental model of human to apply it to the robotic system. It is expected that the suitable mental model for the robots will convey robot's emotion by facial expressions.