Evolutionary design of Multiquadric radial basis functions neural network for face recognition

In this paper, it is proposed to use Multiquadric basis functions at hidden layer of radial basis functions neural networks (RBFNN) for face recognition. The performance of RBFNN depends on the design of the structure of RBFNN, which includes optimal center selection and spread of RBF units, number of neurons at hidden layer, weights etc. Design of hidden layer of RBFNN also includes the choice of basis functions which is proposed to be of Multiquadric basis functions. The shape of Multiquadric basis function plays an important role in the performance of RBFNN in face recognition. A novel evolutionary shape parameter optimization technique inspired by the attractiveness of the natural fireflies is proposed and is used in the design of Multiquadric basis functions for the given face database. The algorithm is tested on two benchmarked face databases ORL and Indian face databases. The proposed technique significantly outperforms the performance of the Gaussian basis functions based RBFNN in terms of face recognition accuracy.