Abstract
This paper introduced an experimental evolution of the effectiveness of utilizing various moments as pattern features in human face technology. In this paper, we apply pseudo Zernike moments (PZM) for recognition of human faces in two-dimensional images, and we compare their performance with other type of moments. The moments that we have used are Zernike moments (ZM), pseudo Zernike moments (PZM) and Legendre moments (LM). We have used shape information for human face localization, also we have used a radial basis function (RBF) neural network as a classifier for this application. The performance of classification is dependent on the moment order as well as the type of moment invariant, but the classification error rate was below %10 in all cases. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that high order degree pseudo Zernike moments contain very useful information about face recognition process, while low order degree moments contain information about face expression.
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