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|Title:||Constructing facial identity surfaces in a nonlinear discriminating space|
|Keywords:||Face recognition;Feature extraction;Linear discriminant analysis;Principal component analysis|
|Citation:||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8-14 December, 2001, Hawaii, USA, 2: pp. 258 - 263, (2001)|
|Abstract:||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, self-shading 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.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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