Face Recognition Using Principal Component Analysis and RBF Neural Networks

In this paper, an efficient method for face recognition using principal component analysis (PCA) and radial basis function (RBF) neural networks is presented. Recently, the PCA has been extensively employed for face recognition algorithms. It is one of the most popular representation methods for a face image. It not only reduces the dimensionality of the image, but also retains some of the variations in the image data. After performing the PCA, the hidden layer neurons of the RBF neural networks have been modelled by considering intra-class discriminating characteristics of the training images. This helps the RBF neural networks to acquire wide variations in the lower-dimensional input space and improves its generalization capabilities. The proposed method has been evaluated using the ATand T (formerly ORL) and UMIST face databases. Experimental results show that the proposed method has encouraging recognition performance.