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Unsupervised Face Alignment by Robust Nonrigid Mapping

J. Zhu, L. Van Gool and S. C. Hoi
Kyoto, Japan, September 2009


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.

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  author = {J. Zhu and L. Van Gool and S. C. Hoi},
  title = {Unsupervised Face Alignment by Robust Nonrigid Mapping},
  booktitle = {ICCV2009},
  year = {2009},
  month = {September},
  publisher = {IEEE},
  keywords = {Face alignment, nonrigid surface recovery}