Face localization by neural networks trained with Zernike moments and Eigenfaces feature vectors. A comparison

Face localization using neural network is presented in this communication. Neural network was trained with two different kinds of feature parameters vectors; Zernike moments and eigenfaces. In each case, coordinate vectors of pixels surrounding faces in images were used as target vectors on the supervised training procedure. Thus, trained neural network provides on its output layer a coordinate's vector (rho,thetas) representing pixels surrounding the face contained in treated image. This way to proceed gives accurate faces contours which are well adapted to their shapes. Performances obtained for the two kinds of training feature parameters were recorded using a quantitative measurement criterion according to experiments carried out on the XM2VTS database.