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Fast and exact bi-directional fitting of active appearance models

Kossaifi, Jean; Tzimiropoulos, Georgios; Pantic, Maja

Fast and exact bi-directional fitting of active appearance models Thumbnail


Jean Kossaifi

Georgios Tzimiropoulos

Maja Pantic


Finding landmarks on objects like faces is a challenging computer vision problem, especially in real life conditions (or in-the-wild) and Active Appearance Models have been widely used to solve it. State-of-the-art algorithms for fitting an AAM to a new image are based on Gauss-Newton (GN) optimization. Recently fast GN algorithms have been proposed for both forward additive and inverse compositional fitting frameworks. In this paper, we propose a fast and exact bi-directional (Fast-Bd) approach to AAM fitting by combining both approaches. Although such a method might appear to increase computational burden, we show that by capitalizing on results from optimization theory, an exact solution, as computationally efficient as the original forward or inverse formulation, can be derived. Our proposed bi-directional approach achieves state-of-the-art performance and superior convergence properties. These findings are validated on two challenging, in-the-wild data sets, LFPW and Helen, and comparison is provided to the state-of-the art methods for Active Appearance Models fitting.


Kossaifi, J., Tzimiropoulos, G., & Pantic, M. (2015). Fast and exact bi-directional fitting of active appearance models.

Conference Name IEEE International Conference on Image Processing (ICIP 2015)
End Date Sep 30, 2015
Publication Date Sep 28, 2015
Deposit Date Jan 29, 2016
Publicly Available Date Jan 29, 2016
Peer Reviewed Peer Reviewed
Keywords active appearance models, Gauss-Newton, forward additive, inverse compositional, bi-directional fitting
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