Wavelet-domain principal component analysis applied to facial similarity trees, caricaturing, and nonlinear illumination-invariant processing

We introduce wavelet-domain principal component analysis and show that it overcomes some of the limitations of space-domain principal component analysis without introducing computationally expensive processing steps. We argue that distance measurement in the wavelet domain is psychovisually more appropriate for judging facial similarity than distance measurement in the space-domain, and build binary facial trees using principal component analysis in the wavelet domain. Compared to caricaturing of space domain similarity trees, caricaturing these trees in the wavelet-domain results in better feature alignment and thus sharper and more credible images. Nonlinear preprocessing makes the approach robust with regard to both global illumination changes and local illumination fluctuations that vary slowly in the spatial domain