Active Appearance Model

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 by the training supervisor.

The approach is widely used for matching and tracking faces and for medical image interpretation.

The algorithm uses the difference between the current estimate of appearance and the target image to drive an optimization process. By taking advantage of the least squares techniques, it can match to new images very swiftly.

It is related to the active shape model.

External links

• Free Tools for experimenting with AAMs from Manchester University.
• Description of AAMs from Manchester University.
• Tim Cootes' home page (one of the original co-inventors of AAMs).
• Mikkel B. Stegmann's home page of AAM-API, open source C++ AAM implementation.

Some reading

• T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. IEEE TPAMI, 23(6):681–685, 2001
• T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Training models of shape from sets of examples. In Proceedings of BMVC’92, pages 266–275, 1992
• S. C. Mitchell, J. G. Bosch, B. P. F. Lelieveldt, R. J. van der Geest, J. H. C. Reiber, and M. Sonka. 3-d active appearance models: Segmentation of cardiac MR and ultrasound images. IEEE Trans. Med. Imaging, 21(9):1167–1178, 2002