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Finger Vein Recognition Using Optimal Partitioning Uniform Rotation Invariant LBP Descriptor


Affiliations
1 Division of Electronic and Information Engineering, Chonbuk National University, Jeonju 561-756, Korea, Republic of
2 Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou, China
 

As a promising biometric system, finger vein identification has been studied widely and many relevant researches have been proposed. However, it is hard to extract a satisfied finger vein pattern due to the various vein thickness, illumination, low contrast region, and noise existing. And most of the feature extraction algorithms rely on high-quality finger vein database and take a long time for a large dimensional feature vector. In this paper, we proposed two block selection methods which are based on the estimate of the amount of information in each block and the contribution of block location by looking at recognition rate of each block position to reduce feature extraction time andmatching time. The specific approach is to find out some local finger vein areas with low-quality and noise, which will be useless for feature description. Local binary pattern (LBP) descriptors are proposed to extract the finger vein pattern feature. Two finger vein databases are taken to test our algorithm performance. Experimental results show that proposed block selection algorithms can reduce the feature vector dimensionality in a large extent.
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  • Finger Vein Recognition Using Optimal Partitioning Uniform Rotation Invariant LBP Descriptor

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Authors

Bang Chao Liu
Division of Electronic and Information Engineering, Chonbuk National University, Jeonju 561-756, Korea, Republic of
Shan Juan Xie
Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou, China
Dong Sun Park
Division of Electronic and Information Engineering, Chonbuk National University, Jeonju 561-756, Korea, Republic of

Abstract


As a promising biometric system, finger vein identification has been studied widely and many relevant researches have been proposed. However, it is hard to extract a satisfied finger vein pattern due to the various vein thickness, illumination, low contrast region, and noise existing. And most of the feature extraction algorithms rely on high-quality finger vein database and take a long time for a large dimensional feature vector. In this paper, we proposed two block selection methods which are based on the estimate of the amount of information in each block and the contribution of block location by looking at recognition rate of each block position to reduce feature extraction time andmatching time. The specific approach is to find out some local finger vein areas with low-quality and noise, which will be useless for feature description. Local binary pattern (LBP) descriptors are proposed to extract the finger vein pattern feature. Two finger vein databases are taken to test our algorithm performance. Experimental results show that proposed block selection algorithms can reduce the feature vector dimensionality in a large extent.