Finger vein recognition using mutual sparse representation classification

Abstract

Sparse representation classification (SRC) is one of the popular methods of classification in biometrics, in which the decision of class for the test sample was based on the class with minimum reconstruction error. As SRC is based on the sparsity of the images, a decision based on reconstruction error is not ideal. In this study, an efficient classification methodology for finger vein recognition called mutual SRC (MSRC) is proposed. MSRC classifies the test sample by a new decision rule which significantly improves the recognition rate of the conventional SRC. By this new decision rule, the classification of the test sample is not only based on the nearest sparse neighbour but also on determining the training sample which considers the test sample as its nearest neighbour (NN). In this method, the training set is selected based on reconstruction error for the test sample, then which training sample considers the test sample as its NN is identified by sparse representation. Increases of 4.67, 10.59, 26.82, and 3.44% in the recognition rates are observed for the proposed MSRC method when compared with conventional SRC using the four public finger vein database.