Integration of Two-Dimensional Kernel Principal Component Analysis Plus Two-Dimensional Linear Discriminant Analysis with Convolutional Neural Network for Finger Vein Recognition

Abstract

High complexity and low recognition rate are two common problems with the current finger vein recognition methods. To solve these problems, this paper integrates two-dimensional kernel principal component analysis (K2DPCA) plus two-dimensional linear discriminant analysis (2DLDA) (K2DPCA+2DLDA) into convolutional neural network (CNN) to recognize finger veins. Considering the row and column correlations of the finger vein image matrix and the classes of finger vein images, the authors adopted K2DPCA and 2DLDA separately for dimensionality reduction and extraction of nonlinear features in row and column directions, producing a dimensionally reduced compressed image without row or column correlation. Taking the dimensionally reduced compressed image as the input, the CNN was introduced to learn higher-level features, making finger vein recognition more accurate and robust. The public dataset of Finger Vein USM (FV-USM) Database was adopted for experimental verification. The results show that the proposed approach effectively overcome the common defects of original image feature extraction: the insufficient feature description, and the redundancy of information. When the training reached 120 epochs, the model basically realized stable convergence, with the loss approaching zero and the recognition rate reaching 97.3%. Compared with two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA), our strategy, which integrates K2DPCA+2DLDA with CNN, achieved a very high recognition rate of finger vein images.