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Sensors 2013, 13(11), 15048-15067; doi:10.3390/s131115048
Article

Finger-Vein Verification Based on Multi-Features Fusion

1,2,* , 2
, 2
, 1
, 3
 and 1
1 School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing 400030, China 2 Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Opto-Electronic Engineering, Chongqing University, Chongqing 400030, China 3 College of Electronic and Automation, Chongqing University of Technology, Chongqing 400045, China
* Author to whom correspondence should be addressed.
Received: 6 July 2013 / Revised: 14 October 2013 / Accepted: 21 October 2013 / Published: 5 November 2013
(This article belongs to the Section Physical Sensors)
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Abstract

This paper presents a new scheme to improve the performance of finger-vein identification systems. Firstly, a vein pattern extraction method to extract the finger-vein shape and orientation features is proposed. Secondly, to accommodate the potential local and global variations at the same time, a region-based matching scheme is investigated by employing the Scale Invariant Feature Transform (SIFT) matching method. Finally, the finger-vein shape, orientation and SIFT features are combined to further enhance the performance. The experimental results on databases of 426 and 170 fingers demonstrate the consistent superiority of the proposed approach.
Keywords: personal identification; finger-vein; scale invariant feature transform; orientation encoding; multi-features fusion personal identification; finger-vein; scale invariant feature transform; orientation encoding; multi-features fusion
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.

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MDPI and ACS Style

Qin, H.; Qin, L.; Xue, L.; He, X.; Yu, C.; Liang, X. Finger-Vein Verification Based on Multi-Features Fusion. Sensors 2013, 13, 15048-15067.

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