NDLI: Bi-directional weighted modular B2DPCA for finger vein recognition

Content Provider	IEEE Xplore Digital Library
Author	Fengxu Guan Kejun Wang Qiuyu Wu
Copyright Year	2010
Description	Author affiliation: College of Automation, Harbin Engineering University, Harbin, China (Fengxu Guan; Kejun Wang; Qiuyu Wu)
Abstract	Due to the restriction of acquisition equipment and other reasons, the finger vein line appeared broken, torsion, translation and other deformation in local area. Therefore, the traditional methods got low recognition accuracy. In this paper, combining the advantages of modular method and bi-directional weighted B2DPCA with eigenvalue normalization; the bidirectional two-dimensional principal component analysis (B2DPCA) algorithm is improved. The bi-directional weighted modular B2DPCA (BWMB2DPCA) algorithm is proposed in order to extract the local feature of finger vein effective. The nearest neighbor classifier was used to distinguish different fingers. Experimental results show that whether in the little training samples, or in the multiple training samples, this method can obtain a better recognition effect than 2DPCA, B2DPCA, WB2DPCA and MB2DPCA.
Starting Page	93
Ending Page	97
File Size	336519
Page Count	5
File Format	PDF
ISBN	9781424465132
e-ISBN	9781424465163
DOI	10.1109/CISP.2010.5646294
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image recognition bidirectional weighted modular B2DPCA Veins Fingers finger vein recognition local feature Feature extraction Eigenvalues and eigenfunctions B2DPCA Covariance matrix
Content Type	Text
Resource Type	Article

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