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An '''active appearance model''' ('''AAM''') is a [[computer vision]] algorithm for matching a [[statistical model]] of object shape and appearance to a new image. They are built during a training phase. A set of images, together with coordinates of landmarks that appear in all of the images, is provided to the training supervisor.
The model was first introduced by Edwards, Cootes and Taylor in the context of face analysis at the 3rd International Conference on Face and Gesture Recognition, 1998.<ref>{{Cite book | last1 = Edwards | first1 = G. J. | last2 = Taylor | first2 = C. J. | last3 = Cootes | first3 = T. F. | doi = 10.1109/AFGR.1998.670965 | chapter = Interpreting face images using active appearance models | title = Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition | pages = 300 | year = 1998 | isbn = 978-0-8186-8344-2
The algorithm uses the difference between the current estimate of appearance and the target image to drive an [[Optimization (mathematics)|optimization]] process.
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* [http://www.mathworks.com/matlabcentral/fileexchange/26706-active-shape-model-asm-and-active-appearance-model-aam Matlab AAM Code] Open-source Matlab implementation of the original AAM algorithm.
* [http://cvsp.cs.ntua.gr/software/AAMtools/ AAMtools] An Active Appearance Modelling Toolbox in Matlab by Dr George Papandreou.
* [https://web.archive.org/web/20150122191259/http://staff.estem-uc.edu.au/roland/research/demolib-home/ DeMoLib] AAM Toolbox in C++ by Dr Jason Saragih and Dr Roland Goecke.
[[Category:Computer vision]]
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