Please use this identifier to cite or link to this item:
Title: Constructing facial identity surfaces in a nonlinear discriminating space
Authors: Li, Y
Gong, S
Liddell, H
Keywords: Face recognition;Feature extraction;Linear discriminant analysis;Principal component analysis
Issue Date: 2001
Publisher: IEEE
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.
ISBN: 0-7695-1272-0
ISSN: 1063-6919
Appears in Collections:Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf308.39 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.