Face and Gesture Recognition for Human-Robot Interaction

Abstract

This chapter presents a vision-based face and gesture recognition system for human-robot interaction. By using subspace method, face and predefined hand poses are classified from the three largest skin-like regions that are segmented using YIQ color representation system. In the subspace method we consider separate eigenspaces for each class or pose. Face is recognized using pose specific subspace method and gesture is recognized using the rulebased approach whenever the combinations of three skin-like regions at a particular image frame satisfy a predefined condition. These gesture commands are sent to robot through TCP/IP wireless network for human-robot interaction. The effectiveness of this method has been demonstrated by interacting with an entertainment robot named AIBO and a humanoid robot Robovie. Human-robot symbiotic systems have been studied extensively in recent years, considering that robots will play an important role in the future welfare society [Ueno, 2001]. The use of intelligent robots encourages the view of the machine as a partner in communication rather than as a tool. In the near future, robots will interact closely with a group of humans in their everyday environment in the field of entertainment, recreation, health-care, nursing, etc. In human-human interaction, multiple communication modals such as speech, gestures and body movements are frequently used. The standard input methods, such as text input via the keyboard and pointer/location information from a mouse, do not provide a natural, intuitive interaction between humans and robots. Therefore, it is essential to create models for natural and intuitive communication between humans and robots. Furthermore, for intuitive gesture-based interaction between human and robot, the robot should understand the meaning of gesture with respect to society and culture. The ability to understand hand gestures will improve the naturalness and efficiency of human interaction with robot, and allow the user to communicate in complex tasks without using tedious sets of detailed instructions. This interactive system uses robot eye’s cameras or CCD cameras to identify humans and recognize their gestures based on face and hand poses. Vision-based face recognition systems have three major components: image processing or extracting important clues (face pose and position), tracking the facial features (related position or motion of face and hand poses), and face recognition. Vision-based face recognition system varies along a number of