Face recognition and tracking for human-robot interaction

This paper presents a design and experimental study of human-robot interaction via face recognition and image tracking. A new architecture is proposed for fast face recognition of family members. In the proposed system, each family member has his/her own RBF neural networks. Each neural network is only responsible for recognizing its trained member. Consequently, the database is small and the processing time required for face recognition is minimized. A recognition rate of 94% has been achieved, an improvement relative to conventional approaches. In order to detect and track a person, we also developed an algorithm for detecting multiple faces in a scene based on division of skin and hair color regions. The face recognition and image tracking system has been integrated to an experimental mobile robot. Practical experiments reveal that the robot demonstrates real-time face recognition and tracking performance.