Transform domain two dimensional and diagonal modular principal component analysis for facial recognition employing different windowing techniques

Spatial domain facial recognition Modular IMage Principal Component Analysis (MIMPCA) has an improved recognition rate compared to the conventional PCA. In the MPCA, face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. In this work, the Transform Domain implementation of MPCA is presented. The facial image has two representations. The Two Dimensional MPCA (TD-2D-MPCA) and the Diagonal matrix MPCA (TD-Dia-MPCA). The sub-images are processed using both non-overlapping and overlapping windows. All the test results, for noise free and noisy images, using ORL, Yale and FERET databases achieved; 99.5%, 99.58% and 97.42% recognition accuracy respectively. Transform Domain implementations yield, computational and storage savings of at least 75% and 99.98%, respectively, compared to spatial domain. Sample results are given. © 2013 IEEE.