Generalized diagonal 2D FLDA for efficient face recognition

Abstract

This paper presents a novel generalized diagonal two-dimensional Fisher's linear discriminant (G-Dia2DFLD) analysis for face representation and recognition. The G-Dia2DFLD method is an extension of the existing DiaFLD method in two aspects. Firstly, the former seeks the maximum class separability by interlacing both the forward and backward diagonals of images simultaneously while the latter seeks optimal projection vectors either from forward or backward diagonal of images. Secondly, the DiaFLD method does not preserve continuity of image regions while generating the diagonal images; resulting partially diagonal images; whereas in G-Dia2DFLD method, this continuity is preserved by generating the diagonal images of the original images. The simulation results on the AT&T and AR databases demonstrate the superiority of the proposed G-Dia2DFLD method over the DiaFLD method and also some existing subspace methods.