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Sequential Active Appearance Model Based on Online Instance Learning
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DOI：10.1109/LSP.2013.2257753.png)
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Abstract
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A hybrid active appearance model (AAM) called sequential AAM (SAAM) based on online instance learning is presented. The subspace of the subject-specific AAM component is initially learned with sequential registration results of first frames, and is periodically updated through incremental principal component analysis and online instance fitting process. A drift correction component of the AAM is also updated during tracking by selecting previous 'good fitting' frame as a reference image. With the model, facial features can be tracked in a video given theirs locations in the first frame and no other information. Experiments show improved fitting accuracy and computation cost compared with other state-of-the-art AAM.
Keywords:
Active appearance model
incremental learning
non-rigid registration
principle component analysis
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