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

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外文摘要：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.

外文关键词：

Vein recognitionNonnegative matrix factorization (NMF)Mapping functionFeature sparsity

作者：

JIA Xu、SUN Fuming、LI Haojie、CAO Yudong

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作者单位：

School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China

School of Software Technology, Dalian University of Technology, Dalian 116024, China

基金：

This work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaNatural Science Foundation of Liaoning Province

项目编号：

61502216No.61572244No.516791162019-ZD-0700

出版年：

2019

DOI：

10.1049/cje.2019.06.003

中国电子杂志(英文版)

CSTPCDCSCDSCIEI

ISSN：1022-4653

年,卷(期)：2019.28(6)

参考文献量33