Generic active appearance models revisited

Tzimiropoulos, Georgios, Alabort-I-Medina, Joan, Zafeiriou, Stefanos and Pantic, Maja (2013) Generic active appearance models revisited. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7726 L (PART 3). pp. 650-663. ISSN 0302-9743

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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 ( ). © 2013 Springer-Verlag.

Additional Information:11th Asian Conference on Computer Vision, ACCV 2012; Daejeon; South Korea; 5 November 2012 through 9 November 2012; Code 96370
Keywords:Active appearance models, Computationally efficient, Facial shape, Generative model, Large margins, Model fitting, Robust algorithm, State-of-the-art algorithms, Image recognition, Parameter estimation, Algorithms
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:11469
Deposited On:08 Jan 2014 10:24

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