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Generic active appearance models revisited

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


Georgios Tzimiropoulos

Joan Alabort-i-Medina

Stefanos Zafeiriou

Maja Pantic


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.

Journal Article Type Article
Publication Date Jan 1, 2013
Journal Lecture Notes in Computer Science
Electronic ISSN 0302-9743
Publisher Humana Press
Peer Reviewed Peer Reviewed
Volume 7726
APA6 Citation Tzimiropoulos, G., Alabort-i-Medina, J., Zafeiriou, S., & Pantic, M. (2013). Generic active appearance models revisited. Lecture Notes in Artificial Intelligence, 7726, doi:10.1007/978-3-642-37431-9_50
Publisher URL
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
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


tzimiroACCV12.pdf (9.7 Mb)

Copyright Statement
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