Hand-Dorsa Vein Recognition by Matching Local Features of Multisource Keypoints
Hand-Dorsa Vein Recognition by Matching Local Features of Multisource Keypoints
复制标题
多源关键点局部特征匹配的手背静脉识别
DOI：
10.1109/tcyb.2014.2360894
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发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Tang Yinhang
中科院分区：
文献类型：
--
作者：
Huang Di;Wang Yiding;Tang Yinhang
As an emerging biometric for people identification, the dorsal hand vein has received increasing attention in recent years due to the properties of being universal, unique, permanent, and contactless, and especially its simplicity of liveness detection and difficulty of forging. However, the dorsal hand vein is usually captured by near-infrared (NIR) sensors and the resulting image is of low contrast and shows a very sparse subcutaneous vascular network. Therefore, it does not offer sufficient distinctiveness in recognition particularly in the presence of large population. This paper proposes a novel approach to hand-dorsa vein recognition through matching local features of multiple sources. In contrast to current studies only concentrating on the hand vein network, we also make use of person dependent optical characteristics of the skin and subcutaneous tissue revealed by NIR hand-dorsa images and encode geometrical attributes of their landscapes, e.g., ridges, valleys, etc., through different quantities, such as cornerness and blobness, closely related to differential geometry. Specifically, the proposed method adopts an effective keypoint detection strategy to localize features on dorsal hand images, where the speciality of absorption and scattering of the entire dorsal hand is modeled as a combination of multiple (first-, second-, and third-) order gradients. These features comprehensively describe the discriminative clues of each dorsal hand. This method further robustly associates the corresponding keypoints between gallery and probe samples, and finally predicts the identity. Evaluated by extensive experiments, the proposed method achieves the best performance so far known on the North China University of Technology (NCUT) Part A dataset, showing its effectiveness. Additional results on NCUT Part B illustrate its generalization ability and robustness to low quality data.
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DOI：
10.11499/sicep.2004.0.115.1
发表时间：
2004-12
期刊：
SICE 2004 Annual Conference
影响因子：
--
作者：
T. Tanaka;N. Kubo
通讯作者：
T. Tanaka;N. Kubo
DOI：
--
发表时间：
2008
期刊：
--
影响因子：
--
作者：
P. Roth;M. Winter
通讯作者：
P. Roth;M. Winter
DOI：
10.1007/978-3-642-14922-1_61
发表时间：
2010-08
期刊：
--
影响因子：
--
作者：
Yiding Wang;Kefeng Li;Jiali Cui;L. Shark;M. Varley
通讯作者：
Yiding Wang;Kefeng Li;Jiali Cui;L. Shark;M. Varley
DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG
DOI：
10.1109/icma.2005.1626888
发表时间：
2005-07
期刊：
IEEE International Conference Mechatronics and Automation, 2005
影响因子：
--
作者：
Yuhang Ding;Dayan Zhuang;Ke-jun Wang
通讯作者：
Yuhang Ding;Dayan Zhuang;Ke-jun Wang