Copula eigenfaces: Semiparametric principal component analysis for facial appearance modeling
Kaufmann D, Schönborn S, Roth V, Vetter T, Egger B (2016)
Publication Type: Conference contribution
Publication year: 2016
Publisher: SciTePress
Pages Range: 50-58
Conference Proceedings Title: VISIGRAPP 2016 - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Event location: Rome
ISBN: 9789897581755
Abstract
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.
Authors with CRIS profile
Involved external institutions
Switzerland (CH)How to cite
APA:
Kaufmann, D., Schönborn, S., Roth, V., Vetter, T., & Egger, B. (2016). Copula eigenfaces: Semiparametric principal component analysis for facial appearance modeling. In Nadia Magnenat-Thalmann, Paul Richard, Lars Linsen, Alexandru Telea, Sebastiano Battiato, Francisco Imai, Jose Braz (Eds.), VISIGRAPP 2016 - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 50-58). Rome, IT: SciTePress.
MLA:
Kaufmann, Dinu, et al. "Copula eigenfaces: Semiparametric principal component analysis for facial appearance modeling." Proceedings of the 11th International Conference on Computer Graphics Theory and Application, GRAPP 2016; Part of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2016, Rome Ed. Nadia Magnenat-Thalmann, Paul Richard, Lars Linsen, Alexandru Telea, Sebastiano Battiato, Francisco Imai, Jose Braz, SciTePress, 2016. 50-58.
BibTeX: Download