Title :
Learnability in human gesture recognition for a partner robot based on computational intelligence
Author :
Kubota, Naoyuki ; Tomioka, Yu.
Author_Institution :
Tokyo Metropolitan Univ., Tokyo
Abstract :
Recently, various types of human-friendly robot have been developed. Such robots should perform voice recognition, gesture recognition, and others. This paper discusses the learning capability of a human gesture recognition method based on computational intelligence. The proposed method is composed of image processing for human face and hand detection based on a steady-state genetic algorithm, an extraction method for human hand motion based on a fuzzy spiking neural network, and an unsupervised classification method for human hand motion based on a self- organizing map. We show several experimental results and discuss their effectiveness.
Keywords :
fuzzy neural nets; genetic algorithms; gesture recognition; image classification; robot vision; self-organising feature maps; computational intelligence; fuzzy spiking neural network; hand detection; human face; human gesture recognition; image processing; partner robot; self-organizing map; steady-state genetic algorithm; unsupervised classification method; voice recognition; Computational intelligence; Face detection; Fuzzy neural networks; Genetic algorithms; Humans; Image processing; Intelligent robots; Motion detection; Speech recognition; Steady-state;
Conference_Titel :
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1339-3
Electronic_ISBN :
978-1-4244-1340-9
DOI :
10.1109/CEC.2007.4424787
