Fuzzy 2D-LDA face recognition based on sub-image

Abstract

This paper proposes a novel method for face recognition, called, sub-image-based fuzzy 2D Linear Discriminant Analysis (subimage-F2DLDA) based on sub-image method, 2-Dimensional Linear Discriminant Analysis (2D-LDA) and fuzzy set theory. We first partition the whole training image set into several different sub-image sets by dividing each image into sub-images and collecting the same location together, and then redefine the within-class matrix and between-class matrix for each sub-image set, which can be computed by incorporating the membership degree matrix using fuzzy k-nearest neighbor(FKNN). Finally, we construct a nearest classifier based on fuzzy 2DLDA for each sub-image set. We construct experiments on Yale A, Extended Yale B and ORL face databases, and the results show that the proposed approach achieves better performance than compared methods on face recognition.