Face Recognition with Radial Basis Function

A generals and an efficient design approach using radial basis function(RBF) neural classifier to cope with small training sets of high dimension , which is a problem frequently encountered in face recognition, is presented in this paper. In order to avoid over fitting and reduce the computational burden, face features are first extracted by the discrete cosine transform method since Principal Component Analysis (PCA) approaches to face recognition are data dependent and computationally expensive. To classify unknown faces they need to match the nearest neighbor in the stored database of extracted face features. In this paper discrete Cosine Transforms(DCT) are used to reduce the dimensionality of face space by truncating high frequency DCT components. Then to avoid the undesired effects of dimensionality reduction techniques such as retaining illumination the resulting face features are further processed by Fisher’s linear discriminant(FLD) technique to acquire lower dimensional dicriminant patterns. The main goal of dimension reduction is to concentrate on vital information while redundant information can be discarded. Various ways are developed to reduce dimensions. Reduction methods can be distinguished into linear and non-linear dimension reduction. In this thesis we shall present the application of face recognition with radial basis function neural networks using discrete cosine transform for featrure extraction and dimension reduction the system perform successfully with the diascrete cosine transform inspite of some problems the part of Fisher’s linear discriminant criteria.