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CVPR
2001
IEEE

Constructing Facial Identity Surfaces in a Nonlinear Discriminating Space

10 years 3 months ago
Constructing Facial Identity Surfaces in a Nonlinear Discriminating Space
Recognising face with large pose variation is more challenging than that in a fixed view, e.g. frontal-view, due to the severe non-linearity caused by rotation in depth, selfshading and self-occlusion. To address this problem, a multi-view dynamic face model is designed to extract the shape-and-pose-free facial texture patterns from multi-view face images. Kernel Discriminant Analysis is developed to extract the significant non-linear discriminating features which maximise the between-class variance and minimise the within-class variance. By using the kernel technique, this process is equivalent to a Linear Discriminant Analysis in a high-dimensional feature space which can be solved conveniently. The identity surfaces are then constructed from these non-linear discriminating features. Face recognition can be performed dynamically from an image sequence by matching an object trajectory and model trajectories on the identity surfaces.
Yongmin Li, Shaogang Gong, Heather M. Liddell
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2001
Where CVPR
Authors Yongmin Li, Shaogang Gong, Heather M. Liddell
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