Discriminant Analysis of Face Images by Local Margin Alignment
Ke Fan · Dianzi Ke-ji Daxue xuebao · 2010
This paper proposes a new approach to perform the discriminant analysis on the labelled high dimensional image data with intra-class sub-manifolds.Real world images are usually taken from the different camera views.Pose,illumination,glasses and gender of the persons taking the facial images usually lead to multi-modality or high curvature of the underlying manifold structures.These variations result in the degraded performance of many existing algorithms.This paper proposes to preserve the within-class local structure,while imposing constrain on the variances only in the directions normal to the between-class margin.The experiments on Yale-B and UMIST face database show that the proposed algorithm outperforms many approaches such as LPP(locality preserving projections) and FDA(fisher discriminant analysis).