Cost-sensitive dual-bidirectional linear discriminant analysis

In most previous cost-sensitive feature extraction methods, the image matrix needs to be converted into vectors. The conversion always leads to a high computation complexity and small sample size problem. To address these issues, we propose a matrix-feature extraction method for face recognition, Cost-sensitive Dual-Bidirectional Linear Discriminant Analysis (CB(2)LDA). It is based on 2D image matrices, which greatly reduces the computation complexity and the probability of falling into small sample size problems. The proposed methods extract 2D matrix features from a diagonal block matrix containing both image matrix A and its transposition AT. With the block matrix, the scatter information in both directions is simultaneously considered in the projections, which helps to preserve the underlying data structure in images. Moreover, it aims to preserve the best cost-weighted discriminative information in the facial images, such that the misclassification costs reach a lower level. The experimental results validate the effectiveness and efficiency of the proposed method. (C) 2019 Elsevier Inc. All rights reserved.