Two-Dimensional Inverse FDA for Face Recognition

Abstract

In this paper, we propose a two-dimensional Inverse Fisher Discriminant Analysis (2DIFDA) method for feature extraction and face recognition. This method combines the ideas of two-dimensional principal component analysis and Inverse FDA and it can directly extracts the optimal projective vectors from 2D image matrices rather than image vectors based on the inverse fisher discriminant criterion. Experiments on the FERET face databases show that the new method outperforms the PCA , 2DPCA, Fisherfaces and the inverse fisher discriminant analysis.