TWO-DIMENSIONAL-ORIENTED LINEAR DISCRIMINANT ANALYSIS FOR FACE RECOGNITION

In this paper, a new statistical projection-based method called Two-Dimensional Oriented Linear Discriminant Analysis (2DO-LDA) is presented. While in the Fisherfaces method the 21) image matrices are first transformed into 1D vectors by merging their rows of pixels, 2DO-LDA is directly applied on matrices, as 2D-PCA. Within and between-class image covariance matrices are generalized, and 2DO-LDA aims at finding a projection space jointly maximizing the second and minimizing the first by considering a generalized Fisher criterion defined on image matrices. A series of experiments was performed on various face image databases in order to evaluate and compare the effectiveness and robustness of 2DO-LDA to 2D-PCA and the Fisherfaces method. The experimental results indicate that 2DO-LDA is more efficient than both 2D-PCA and LDA when dealing with variations in lighting conditions, facial expression and head pose.