Hand vein recognition based on oriented gradient maps and local feature matching

Authors:
Di Huang;Yinhang Tang;Yiding Wang;Liming Chen;Yunhong Wang
Affiliations:
Laboratory of Intelligence Recognition and Image Processing, School of Computer Science and Engineering, Beihang University, Beijing, China;Laboratory of Intelligence Recognition and Image Processing, School of Computer Science and Engineering, Beihang University, Beijing, China;College of Info. Eng., North China University of Technology, Beijing, China;CNRS, Ecole Centrale Lyon, LIRIS, Université de Lyon, Lyon, France;Laboratory of Intelligence Recognition and Image Processing, School of Computer Science and Engineering, Beihang University, Beijing, China
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Year:
2012

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Abstract

The hand vein pattern as a biometric trait for identification has attracted increasing interests in recent years thanks to its properties of uniqueness, permanence, non-invasiveness as well as strong immunity against forgery. In this paper, we propose a novel approach for back of the hand vein recognition. It first makes use of Oriented Gradient Maps (OGMs) to represent the Near-Infrared (NIR) hand vein images, simultaneously highlighting the distinctiveness of vein patterns and texture of their surrounding corium, in contrast to the state-of-the-art studies that only focused on the segmented vein region. SIFT based local matching is then performed to associate the keypoints between corresponding OGM pairs of the same subject. The proposed approach was benchmarked on the NCUT database consisting of 2040 NIR hand vein images from 102 subjects. The experimental results clearly demonstrate the effectiveness of our approach.