BMVC 2004, Kingston, 7th-9th Sept, 2004
Generic vs. Person Specific Active Appearance Models
R. Gross, I. Matthews, and S. Baker (Carnegie Mellon University)
Active Appearance Models (AAMs) are generative parametric models
that have been successfully used in the past to model faces. Anecdotal evidence,
however, suggests that the performance of an AAM built to model the
variation in appearance of a single person across pose, illumination, and expression
(Person Specific AAM) is substantially better than the performance
of an AAM built to model the variation in appearance of many faces, including
unseen subjects not in the training set (Generic AAM). In this paper we
present an empirical evaluation that shows that Person Specific AAMs are,
as expected, both easier to build and more robust to fit than Generic AAMs.
Moreover, we show that: (1) building a generic shape model is far easier
than building a generic appearance model, and (2) the shape component is
the main cause of the reduced fitting robustness of Generic AAMs. We then
proceed to describe two refinements to Generic AAMs to improve their performance:
(1) a refitting procedure to improve the quality of the ground-truth
data used to build the AAM and (2) a new fitting algorithm. For both refinements
we demonstrate vastly improved fitting performance.
(pdf article)