Copula Eigenfaces - Semiparametric Principal Component Analysis for Facial Appearance Modeling
Bernhard Egger, Dinu Kaufmann, Sandro Schönborn, Volker Röth, Thomas R. Vetter · 2016
Principal component analysis is a ubiquitous method in parametric appearance modeling for describing dependency and variance in a data set. The method requires that the observed data be Gaussian-distributed. We show that this requirement is not fulfilled in the context of analysis and synthesis of facial appearance. The model mismatch leads to unnatural artifacts which are severe to human perception. In order to prevent these artifacts, we propose to use a semiparametric Gaussian copula model, where dependency and variance are modeled separately. The Gaussian copula enables us to use arbitrary Gaussian and non-Gaussian marginal distributions. The new flexibility provides scale invariance and robustness to outliers as well as a higher specificity in generated images. Moreover, the new model makes possible a combined analysis of facial appearance and shape data. In practice, the proposed model can easily enhance the performance obtained by principal component analysis in existing pipelines: The steps for analysis and synthesis can be implemented as convenient pre- and post-processing steps.