Face Recognition Based on Radial Basis Function Neural Networks using Subtractive Clustering Algorithm

We propose a new method for learning of radial basis function (RBF) neural networks which is based on subtractive clustering algorithm (SCA) and its application to face recognition. The rationale behind the method learning center parameters of RBF by means of SCA is that, for SCA, number of data points and calculation is irrelevant to the dimension of the clustered information and face images are high-dimensional. The algorithm can effectively improve training speed and accuracy. Experiments on face recognition using ORL database show feasibility of the method. Results present that RBF neural networks classifier using proposed algorithm is more precise and faster than corresponding one using general K-means clustering algorithm