Learning AAM fitting through simulation

Jason Saragih, Roland Goecke

    Research output: Contribution to journalArticlepeer-review

    61 Citations (Scopus)


    The active appearance model (AAM) is a powerful method for modeling and segmenting deformable visual
    objects. The utility of the AAM stems from two fronts: its compact representation as a linear object class
    and its rapid fitting procedure, which utilizes fixed linear updates. Although the original fitting procedure
    works well for objects with restricted variability when initialization is close to the optimum, its efficacy
    deteriorates in more general settings, with regards to both accuracy and capture range. In this paper, we
    propose a novel fitting procedure where training is coupled with, and directly addresses, AAM fitting in
    its deployment. This is achieved by simulating the conditions of real fitting problems and learning the
    best set of fixed linear mappings, such that performance over these simulations is optimized. The power
    of the approach does not stem from an update model with larger capacity, but from addressing the whole
    fitting procedure simultaneously. To motivate the approach, it is compared with a number of existing
    AAM fitting procedures on two publicly available face databases. It is shown that this method exhibits
    convergence rates, capture range and convergence accuracy that are significantly better than other linear
    methods and comparable to a nonlinear method, whilst affording superior computational efficiency
    Original languageEnglish
    Pages (from-to)2628-2636
    Number of pages9
    JournalPattern Recognition
    Issue number11
    Publication statusPublished - 2009


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