Bilinear Discriminant Analysis for Face Recognition

In this paper, a new statistical projection method called Bilinear Discriminant Analysis (BDA) is presented. The proposed method efficiently combines two complementary versions of Two-Dimensional-Oriented Linear Discriminant Analysis (2DoLDA), namely Column-Oriented Linear Discriminant Analysis (CoLDA) and Row-Oriented Linear Discriminant Analysis (RoLDA), through an iterative algorithm using a generalized bilinear projectionbased Fisher criterion. A series of experiments was performed on various international face image databases in order to evaluate and compare the effectiveness of BDA to RoLDA and CoLDA. The experimental results indicate that BDA is more efficient than RoLDA, CoLDA and 2DPCA for the task of face recognition, while leading to a significant dimensionality reduction.