Unsupervised Face Alignment by Robust Nonrigid Mapping

• Main Authors: ZHU, Jianke, VAN GOOL, Luc, HOI, Steven C. H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Lucas-Kanade image registration; affine transformations; robust nonrigid mapping; robust optimization scheme; unsupervised facial image alignment; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2371; https://ink.library.smu.edu.sg/context/sis_research/article/3371/viewcontent/zhu_iccv09.pdf
• Institution: Singapore Management University

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.