A Generalized Kernel Fisher Discriminant Framework Used for Feature Extraction and Face Recognition

摘要

In this paper, an improved Kernel Fisher Discriminant (KFD) method is used in face recognition. A Generalized Kernel Fisher Discriminant Analysis (GKFD) is proposed to make the most of two kinds of discriminant information in "double discriminant subspaces". It can also uniform the discriminant functions in two subspaces of DSDA. Experimental results on ORL face database show the feasibility of the suggested method.