Multi-stage fuzzy evaluation in evolutionary robot vision for face detection
This paper proposes a method of evolutionary robot vision based on a local genetic algorithm based on clustering (LGAC) and multi-stage fuzzy evaluation. In order to improve the communication capability of human-friendly partner robots, the human face should be extracted 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 bottomup processing, and local genetic algorithm based on clustering for template matching in human face detection as a topdown processing. In order to improve the performance of the human face detection, we use multi-stage fuzzy evaluation for evaluating the degree of human face. Finally, we show several experimental results and discuss the effectiveness of the proposed method.
robot vision face detection partner robots evolutionary computation fuzzy theory