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Copula Eigenfaces with Attributes: Semiparametric Principal Component Analysis for a Combined Color, Shape and Attribute Model

Egger, Bernhard and Kaufmann, Dinu and Schönborn, Sandro and Roth, Volker and Vetter, Thomas. (2017) Copula Eigenfaces with Attributes: Semiparametric Principal Component Analysis for a Combined Color, Shape and Attribute Model. Communications in Computer and Information Science, 693. pp. 95-112.

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Official URL: https://edoc.unibas.ch/59210/

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Abstract

Principal component analysis is a ubiquitous method in parametric appearance modeling for describing dependency and variance in datasets. The method requires the observed data to 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. As a remedy, we use a semiparametric Gaussian copula model, where dependency and variance are modeled separately. This model enables us to use arbitrary Gaussian and non-Gaussian marginal distributions. Moreover, facial color, shape and continuous or categorical attributes can be analyzed in an unified way. Accounting for the joint dependency between all modalities leads to a more specific face model. In practice, the proposed model can enhance performance of principal component analysis in existing pipelines: The steps for analysis and synthesis can be implemented as convenient pre- and post-processing steps.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Computergraphik Bilderkennung (Vetter)
UniBasel Contributors:Vetter, Thomas and Egger, Bernhard
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Springer
ISSN:1865-0929
Note:Publication type according to Uni Basel Research Database: Journal article -- The final publication is available at Springer, see DOI link
Language:English
Identification Number:
Last Modified:09 Feb 2018 12:37
Deposited On:09 Feb 2018 12:37

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