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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 2373818, 14 pages
Research Article

SIFT Based Vein Recognition Models: Analysis and Improvement

School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221006, China

Correspondence should be addressed to Guoqing Wang; moc.liamtoh@tmucgniqouggnaw

Received 19 November 2016; Revised 15 April 2017; Accepted 9 May 2017; Published 7 June 2017

Academic Editor: Hiro Yoshida

Copyright © 2017 Guoqing Wang and Jun Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Scale-Invariant Feature Transform (SIFT) is being investigated more and more to realize a less-constrained hand vein recognition system. Contrast enhancement (CE), compensating for deficient dynamic range aspects, is a must for SIFT based framework to improve the performance. However, evidence of negative influence on SIFT matching brought by CE is analysed by our experiments. We bring evidence that the number of extracted keypoints resulting by gradient based detectors increases greatly with different CE methods, while on the other hand the matching result of extracted invariant descriptors is negatively influenced in terms of Precision-Recall (PR) and Equal Error Rate (EER). Rigorous experiments with state-of-the-art and other CE adopted in published SIFT based hand vein recognition system demonstrate the influence. What is more, an improved SIFT model by importing the kernel of RootSIFT and Mirror Match Strategy into a unified framework is proposed to make use of the positive keypoints change and make up for the negative influence brought by CE.