Determining Correspondences for Statistical Models of Facial Appearence

K.N.Walker, T.F.Cootes, C.J.Taylor

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.