Abstract: AAM has been successfully applied to face alignment,
but its performance is very sensitive to initial values. In case the initial
values are a little far distant from the global optimum values, there
exists a pretty good possibility that AAM-based face alignment may
converge to a local minimum. In this paper, we propose a progressive
AAM-based face alignment algorithm which first finds the feature
parameter vector fitting the inner facial feature points of the face and
later localize the feature points of the whole face using the first
information. The proposed progressive AAM-based face alignment
algorithm utilizes the fact that the feature points of the inner part of the
face are less variant and less affected by the background surrounding
the face than those of the outer part (like the chin contour). The
proposed algorithm consists of two stages: modeling and relation
derivation stage and fitting stage. Modeling and relation derivation
stage first needs to construct two AAM models: the inner face AAM
model and the whole face AAM model and then derive relation matrix
between the inner face AAM parameter vector and the whole face
AAM model parameter vector. In the fitting stage, the proposed
algorithm aligns face progressively through two phases. In the first
phase, the proposed algorithm will find the feature parameter vector
fitting the inner facial AAM model into a new input face image, and
then in the second phase it localizes the whole facial feature points of
the new input face image based on the whole face AAM model using
the initial parameter vector estimated from using the inner feature
parameter vector obtained in the first phase and the relation matrix
obtained in the first stage. Through experiments, it is verified that the
proposed progressive AAM-based face alignment algorithm is more
robust with respect to pose, illumination, and face background than the
conventional basic AAM-based face alignment algorithm.