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Generic active appearance models revisited

Tzimiropoulos, Georgios; Alabort-i-Medina, Joan; Zafeiriou, Stefanos; Pantic, Maja

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Authors

Georgios Tzimiropoulos

Joan Alabort-i-Medina

Stefanos Zafeiriou

Maja Pantic



Abstract

The proposed Active Orientation Models (AOMs) are generative models of facial shape and appearance. Their main differences with the well-known paradigm of Active Appearance Models (AAMs) are (i) they use a different statistical model of appearance, (ii) they are accompanied by a robust algorithm for model fitting and parameter estimation and (iii) and, most importantly, they generalize well to unseen faces and variations. Their main similarity is computational complexity. The project-out version of AOMs is as computationally efficient as the standard project-out inverse compositional algorithm which is admittedly the fastest algorithm for fitting AAMs. We show that not only does the AOM generalize well to unseen identities, but also it outperforms state-of-the-art algorithms for the same task by a large margin. Finally, we prove our claims by providing Matlab code for reproducing our experiments ( http://ibug.doc.ic.ac.uk/resources ). © 2013 Springer-Verlag.

Conference Name Computer Vision -- ACCV 2012
Conference Location Daejeon, South Korea
Start Date Nov 5, 2012
End Date Nov 9, 2012
Publication Date Apr 11, 2013
Deposit Date Feb 1, 2016
Publicly Available Date Feb 1, 2016
Publisher Springer Verlag
Volume 7726
Pages 650-663
Series Title Lecture Notes in Computer Science
Series ISSN 1611-3349
Book Title ACCV 2012: Computer Vision – ACCV 2012
ISBN 9783642374302
DOI https://doi.org/10.1007/978-3-642-37431-9_50
Public URL https://nottingham-repository.worktribe.com/output/1005649
Publisher URL http://link.springer.com/chapter/10.1007/978-3-642-37431-9_50
Additional Information Chapter in: Computer Vision – ACCV 2012 : 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, revised selected papers, Part III. ISBN 978-3-642-37430-2

The final publication is available at Springer via https://doi.org/10.1007/978-3-642-37431-9_50

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