Face recognition using transform features and neural networks

In this paper we considered face recognition using two Radial Basis Function Network (RBFN) architectures and compared performance with the nearest neighbor algorithm. Performance was also evaluated for feature vectors extracted from face images by using principal component analysis as well as wavelet transform. Raw recognition rates as well as rates with confidence measures were considered. in the RBFN1 architecture, one network was used to discriminate among the classes, while the RBFN2 architecture used one network per class. From the point of view of computations RBFN2 was more efficient than RBFN1 with PCA feature vectors, but its recognition performance was slightly worse than RBFN1. Other experiments showed that RBFN1 was largely superior to the NNA when the amount of computations in both methods was similar. With the use of wavelet features, performance dropped 5-10% in relation to features extracted by PCA. However, in a given implementation, this must be weighed in conjunction with the advantages of using wavelet features, namely, no storage is required for eigenvectors, and they are simpler to compute. (C) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.