Generalized 2D Fisher Discriminant Analysis
Heng Kong, E.K. Teoh, Jingguo Wang, Chandra Kambhamettu · 2005
To solve the Small Sample Size (SSS) problem, the recent linear discriminant analysis using the 2D matrix-based data representation model has demonstrated its superiority over that using the conventional vector-based data representation model in face recognition [7]. But the explicit reason why the matrix-based model is better than vectorized model has not been given until now. In this paper, a framework of Generalized 2D Fisher Discriminant Analysis (G2DFDA) is proposed. Three contributions are included in this framework: 1) the essence of these ’2D ’ methods is analyzed and their relationships with conventional ’1D ’ methods are given, 2) a Bilateral and 3) a Kernel-based 2D Fisher Discriminant Analysis methods are proposed. Extensive experiment results show its excellent performance. 1