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Open AccessArticle

Finger Vein Recognition Based on Personalized Weight Maps

School of Computer Science and Technology, Shandong University, Jinan 250101, China
Author to whom correspondence should be addressed.
Sensors 2013, 13(9), 12093-12112;
Received: 18 July 2013 / Revised: 21 August 2013 / Accepted: 3 September 2013 / Published: 10 September 2013
(This article belongs to the Section Physical Sensors)
PDF [534 KB, uploaded 21 June 2014]


Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs). The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition. View Full-Text
Keywords: finger vein recognition; binary pattern; Hamming distance; personalized weight map; general framework finger vein recognition; binary pattern; Hamming distance; personalized weight map; general framework
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Yang, G.; Xiao, R.; Yin, Y.; Yang, L. Finger Vein Recognition Based on Personalized Weight Maps. Sensors 2013, 13, 12093-12112.

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