Designing a Fuzzy RBF Neural Network with Optimal Number of Neuron in Hidden Layer and Effect of Signature Shape for Persian Signature Recognition by Zernike Moments and PCA

This paper presents an efficient method for Persian signature recognition based on Fuzzy RBF neural network (FRBF). A new training method will be presented which had a very low error rates in Persian signature recognition. In this training algorithm, connection weights, centers, width and number of RBF units will be determined during training phase. FCM algorithm will be used for initializing parameters. The membership of input patterns and distance from centers in each RBF unit calculate cost function for each input pattern. In this study Zernike Moment (ZM) and Principle Component Analysis (PCA) have been used as features. Also has been inspected effect of signature shape in system error. Simulation results on signature database from Persian peoples which contains 200 pictures indicate that the proposed system not only has a low error rate, but also determine the optimal number of RBF units.