Generic active appearance models revisited

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

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.

Citation

Tzimiropoulos, G., Alabort-i-Medina, J., Zafeiriou, S., & Pantic, M. (2012, November). Generic active appearance models revisited. Presented at Computer Vision -- ACCV 2012, Daejeon, South Korea

• Presentation Conference Type: Edited Proceedings
• Conference Name: Computer Vision -- ACCV 2012
• 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