Unsupervised face alignment by robust nonrigid mapping

We propose a novel approach to unsupervised facial image alignment. Differently from previous approaches, that are confined to affine transformations on either the entire face or separate patches, we extract a nonrigid mapping between facial images. Based on a regularized face model, we frame unsupervised face alignment into the Lucas-Kanade image registration approach. We propose a robust optimization scheme to handle appearance variations. The method is fully automatic and can cope with pose variations and expressions, all in an unsupervised manner. Experiments on a large set of images showed that the approach is effective. ©2009 IEEE.Zhu J. , Van Gool L., Hoi S.C.H., ''Unsupervised face alignment by robust nonrigid mapping'', 12th IEEE international conference on computer vision, ICCV 2009, September 27 - October 4, 2009, Kyoto, Japan.status: publishe