Sequential Active Appearance Model Based on Online Instance Learning

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.