[1]HU Na,MA Hui,ZHAN Tao.Finger vein recognition method combining LBP texture feature and B2DPCA technology[J].CAAI Transactions on Intelligent Systems,2019,14(3):533-540.[doi:10.11992/tis.201801014]
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Finger vein recognition method combining LBP texture feature and B2DPCA technology

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 3 Page number: 533-540 Column: 学术论文—机器感知与模式识别 Public date: 2019-05-05
Title:
Finger vein recognition method combining LBP texture feature and B2DPCA technology
Author(s):
HU Na;  MA Hui;  ZHAN Tao
College of Electronic Engineering, Heilongjiang University, Harbin 150001, China
Keywords:
finger vein recognition; feature extraction; local binary patterns; two-dimensional principal component; bidirectional two-dimensional principal component analysis; euclidean distance; image feature vector; dimensionality reduction
CLC:
TP391.4
DOI:
10.11992/tis.201801014
Abstract:
By considering the sensitivity of the traditional local binary pattern (LBP) algorithms while varying the illumination, this study proposes a finger vein recognition method using a rotation invariant LBP operator and B2DPCA. This method initially extracts the LBP texture spectrum feature of the image block of a finger vein, uses a bidirectional two-dimensional main component analysis method to effectively reduce the dimension of the eigenmatrix comprising the LBP eigenvectors, and finally classifies the final samples by comparing the Euclidean distance between the vein image eigenvectors that are to be identified and the eigenvectors of other samples after dimension reduction. The experiments were implemented on the finger vein image databases obtained from the Tianjin Intelligence Laboratory and from the FV-USM database of the University of Science, Malaysia. Further, eight methods with different numbers of training samples are compared, which exhibit that the fusion features that are proposed by this study perform considerably better than the single LBP operator, single traditional dimension-reduced methods, and the fusion of LBP and traditional dimension-reduced algorithms. Additionally, the recognition rate of the generated method was observed to significantly improve. This indicated that the analysis method proposed in this study is proper and effective.
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