基于有监督双正则NMF的静脉识别算法

doi:10.11896/j.issn.1002-137X.2018.08.051

Abstract

Abstract: In order to make the extracted vein feature have good clustering performance and thus be more conductive to correct identification,this paper proposed a recognition algorithm based on supervised Nonnegative Matrix Factorization (NMF).Firstly,vein image is divided into blocks,and the original vein feature can be acquired by fusing all sub image features.Secondly,the sparsity and clustering property of feature vectors areregarded as two regularization terms,and the original NMF model is improved.Then,gradient descent method is used to solve the improved NMF model,and feature optimization and dimension reduction can be achieved.Finally,by using nearest neighbor algorithm to match new vein features,the recognition results can be acquired.Experiment results show that the obtained false accept rate (FAR) and false reject rate (FRR) of the proposed recognition algorithm can be reached 0.02 and 0.03 respectively for three vein databases,in addition,the recognition time of 2.89 seconds can meet real-time requirement.

Key words: Biological feature, Feature dimension reduction, Nonnegative Matrix Factorization, Sparse representation, Vein recognition

CLC Number:

TP391.4

JIA Xu, SUN Fu-ming, LI Hao-jie, CAO Yu-dong. Vein Recognition Algorithm Based on Supervised NMF with Two Regularization Terms[J].Computer Science, 2018, 45(8): 283-287.

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Vein Recognition Algorithm Based on Supervised NMF with Two Regularization Terms

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