Hand Vein Recognition Algorithm Based on NMF with Sparsity and Clustering Property Constraints in Feature Mapping Space

Abstract

Most of the existed vein features are lack of robustness to light intensity variation, and some algorithms rely on the specified vein data sets, which leads to the limitation of real applications. To solve the problems, we propose a novel vein recognition algorithm based on Nonnegative matrix factorization (NMF) with double regularization terms. The innovations of our algorithm are mainly reflected in the following two aspects: in order to improve feature robustness, a novel feature mapping function is designed to map the initial Histogram of oriented gradient (HOG) feature to a new space; to enhance the recognition performance, an effective NMF model is presented, which not only reduces feature dimension, but also optimizes the feature sparsity and clustering property simultaneously. Experiments show that the proposed algorithm can achieve satisfactory results in terms of False rejection rate (FRR) and False acceptance rate (FAR), which indicates that our algorithm is valuable for other classification problems.