Pseudo RBF Network for Position Independent Hand Posture Recognition System

This paper proposes a new neuron architecture for a network similar to the radial basis function (RBF) network. The network with the proposed neuron, which we call a pseudo RBF network, is aimed for pattern classifications. Same as the conventional RBF network, each neuron in the hidden layer of the network is associated with a single cluster that represents a subclass. The proposed neuron effectively evaluates the possibility of the input vector belonging to its cluster. The pseudo RBF network with the proposed neuron is applied to a hand posture recognition system. Input image is preprocessed through horizontal/vertical projection followed by discrete Fourier transforms (DFTs) that calculate the magnitude spectrum. The magnitude spectrum is used as the feature vector to be fed to the network. Use of the magnitude spectrum makes the system very robust against the position changes of the hand image. The simulation results show that the average recognition rate of the system is 98% even though the hand positions are changed randomly.