Face Recognition Using an RBF Neural Classifier with Hybrid Learning

Abstract

A general and efficient design approach using an RBF neural clas-sifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented in this chapter. In order to avoid overfitting and reduce the computational burden, face features are first extracted by the Principal Component Analysis (PCA) method. Then, the resulting features are further processed by the Fisher’s Linear Discriminant (FLD) technique to acquire lower-dimensional discriminant patterns. A novel paradigm is proposed whereby data information is encapsulated in determining the structure and initial parameters of the RBF neural classifier before learning takes place. A hybrid learning algorithm is used to train the RBF neural networks so that the dimension of the search space is drastically reduced in the gradient paradigm. Simulation results conducted on the ORL database show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.