Basis Selection for 2DLDA-Based Face Recognition Using Fisher Score

Abstract

Two-Dimension Linear Discriminant Analysis (2DLDA) becomes a popular technique for face recognition due to its effectiveness in both accuracy and computational cost. Furthermore, there has been shown that 2DLDA reduces only the row direction of the data. This gives a rise to a new technique, (2D)²LDA. (2D)²LDA performs 2DLDA on the row direction and conducts Alternate 2DLDA on the column direction of the data. Although the eigenvalues associated with eigenvectors simply show the discriminative power of the subspace spanned by the corresponding eigenvectors, there are some evidences indicate the eigenvector with high eigenvalue may correspond to noise signal such as pose, illumination or expression and the eigenvector with high discriminative power may have a low eigenvalue due to its closeness to the null space of the training data. By these reasons, we may improve the performace of 2DLDA-based techniques by properly reordering the importance of their eigenvectors. In this paper, we propose a technique to solve this problem; we use the Subspace Scoring with the Fisher Criterion to rerank the discriminative power of the subspace spanned by certain eigenvectors. The experimental results show that our method makes an improvement to 2DLDA and (2D)²LDA in accuracy. We also combine our proposed method with the wrapper method to determine the target dimension for further use.