30-11-2009, 03:08 PM
The performance of face recognition systems that use two-dimensional (2D) images is dependent on consistent conditions such as lighting, pose and facial expression. A multi-view face recognition system is being developed, which utilizes three-dimensional (3D) information about the face, along with the facial texture, to make the system more robust to those variations. A procedure is presented for constructing a database of 3D face models and matching this database to 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. A robust similarity metric is defined for matching. To address the non-rigid facial movement, such as expressions, we present a facial surface modeling and matching scheme to match 2.5D test scans in the presence of both non-rigid deformations and large pose changes (multiview) to a neutral expression 3D face model. A geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A personspecific (3D) deformable model is built for each subject in the gallery w.r.t. the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and large pose changes simultaneously
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http://biometrics.cse.msu.edu/Publicatio...WACV05.pdf
http://biometrics.cse.msu.edu/Publicatio...WACV05.pdf
