Invariant neural-network based face detection with orthogonal Fourier-Mellin moments

Abstract

We apply the orthogonal Fourier-Mellin moments (OFMMs) to the specific problem of fully translation-, scale- and in-plane rotation-invariant detection of human faces in 2D static color images, and compare their performance with that of the generalized Hu's moments or non-orthogonal FMMs. The OFMMs have the advantages of non-redundancy of information, robustness with respect to noise and the ability to reconstruct the original object. Color segmentation is first performed in nine different chrominance spaces by use of two human skin chrominance models. For feature extraction in the segmented images, the same number of OFMMs are used (as for the FMMs) as the input vector to a multilayer perceptron neural network to distinguish faces from distracters. It is shown that, at least in the specific problem of face detection from segmented images, for the same set of test images, there is no significant advantage over the FMMs in using the OFMMs, and that in practice both types of moments may be used. Possible explanations for such results are presented.