Face Recognition Using Principal Component Analysis and RBF Neural Networks

S. Thakur, J.K. Sing, D.K. Basu, M. Nasipuri, M. Kundu
2008 2008 First International Conference on Emerging Trends in Engineering and Technology  
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
more » ... 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 AT&T (formerly ORL) and UMIST face databases. Experimental results show that the proposed method has encouraging recognition performance.
doi:10.1109/icetet.2008.104 dblp:conf/icetet/ThakurSBNK08 fatcat:kfrkko7ourbxtbnqjhdwt5zghq