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.

• Type: Conference
• Year: 2001
• Where: CVPR
• Authors: Yongmin Li, Shaogang Gong, Heather M. Liddell
• Keywords: Computer Vision, CVPR 2001, Kernel Discriminant Analysis, Multi-view Dynamic Face, Multi-view Face Images, Non-linear Discriminating Features, Significant Non-linear Discriminating