Evolutionary Robot Vision and Fuzzy Evaluation for Natural Communication of Partner Robots

This paper proposes a method of evolutionary robot vision based on a steady-state genetic algorithm and fuzzy evaluation. In order to improve the communication capability of human-friendly partner robots, the perception of human face should be performed as correctly as possible. First, we discuss the concept of evolutionary robot vision in dynamic environments. Next, we propose growing neural gas for preprocessing as a bottom-up processing, and steadystate genetic algorithm for template matching in human face recognition as a top-down processing. In order to improve the performance of the human face recognition, we use fuzzy evaluation for evaluating the degree of human face. Finally, we show several experimental results and discuss the effectiveness of the proposed method. Keywords-- Face Recognition, Evolutionary Computation, Robot Vision, Fuzzy Theory, Partner Robots

• Type: Journal
• Year: 2009
• Where: EUSFLAT
• Authors: Akihiro Yorita, Naoyuki Kubota
• Keywords: EUSFLAT 2009, Evolutionary Robot Vision, Fuzzy Logic, Human Face, Robot Vision