Using Radial Basis Function Networks for Hand Gesture Recognition
This chapter is about a data-glove/neural network system as a powerful input device for virtual reality and multi media applications. In contrast to conventional keyboards, space balls, and two-dimensional mice, which allow for only rudimental inputs, the data-glove system allows the user to present the system with a rich set of intuitive commands. Previous research has employed different neural networks to recognize various hand gestures. Due to their on-line adaptation capabilities, radial basis function networks are preferable to backpropagation. Unfortunately, the latter have shown better recognition rates. This chapter describes the application and discusses the performance of various radial basis function networks for hand gesture recognition. This chapter furthermore applies evolutionary algorithms to fine tune pre-learned radial basis function networks. After optimization, the network achieves a recognition rate of up to 100%, and is therefore comparable or even better than that of back-propagation networks.
KeywordsRadial Basis Function Evolution Strategy Recognition Rate Gesture Recognition Radial Basis Function Network
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