Determining Correspondences for Statistical Models of Facial Appearence
K.N.Walker, T.F.Cootes, C.J.Taylor
Abstract
In order to build a statistical model of facial appearance we require
a set of images, each with a consistent set of landmarks. We address
the problem of automatically placing a set of landmarks to define the
correspondences across an image set. We can estimate correspondences
between any pair of images by locating salient points on one and finding
their corresponding position in the second. However, we wish to determine
a globally consistent set of correspondences across all the images. We
present an iterative scheme in which these pair-wise correspondences
are used to determine a global correspondence across the entire set. We
show results on several training sets, and demonstrate that Appearance
Models trained on the correspondences are of higher quality than one built
from hand marked images.