Study of hand-dorsa vein recognition

Authors:
Yiding Wang;Kefeng Li;Jiali Cui;Lik-Kwan Shark;Martin Varley
Affiliations:
College of Information Engineering, North China University of Technology, Beijing, China;College of Information Engineering, North China University of Technology, Beijing, China;College of Information Engineering, North China University of Technology, Beijing, China;School of Computing, Engineering and Physical Sciences, University of Central Lancashire, Preston, United Kingdom;School of Computing, Engineering and Physical Sciences, University of Central Lancashire, Preston, United Kingdom
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

A new hand-dorsa vein recognition method based on Partition Local Binary Pattern (PLBP) is presented in this paper. The proposed method employs hand-dorsa vein images acquired from a low-cost, near infrared device. After preprocessing, the image is divided into sub-images. LBP uniform pattern features are extracted from all the sub-images, which are combined to form the feature vector for token vein texture features. The method is assessed using a similarity measure obtained by calculating the Chi square statistic between the feature vectors of the tested sample and the target sample. Integral histogram method, original LBP and Partition LBP with 16, 32, 64 sub-images are tested on a database of 2040 images from 102 individuals built up by a custom-made acquisition device. The experimental results show that Partition LBP performs better than original LBP, Circular Partition LBP performs better than Rectangular Partition LBP, and when the image was divided into 32 performs better than others.