NDLI: Improving feature based dorsal hand vein recognition through Random Keypoint Generation and fine-grained matching

Content Provider	IEEE Xplore Digital Library
Author	Renke Zhang Di Huang Yiding Wang Yunhong Wang
Copyright Year	2015
Description	Author affiliation: State Key Lab. of Virtual Reality Technol. & Syst., Beihang Univ., Beijing, China (Yunhong Wang) \|\| State Key Lab. of Software Dev. Environ., Beihang Univ., Beijing, China (Renke Zhang; Di Huang) \|\| Coll. of Inf. Eng., North China Univ. of Technol., Beijing, China (Yiding Wang)
Abstract	Recently, SIFT-like approaches have shown their advantages of performance and robustness in dorsal hand vein recognition. This paper presents a novel method to recognize the vein pattern of the dorsal hand, which discusses two important issues in the SIFT-like framework, i.e. keypoint detection and matching. For the former, a Gaussian Distribution based Random Keypoint Generation method (GDRKG) is proposed to localize a sufficient set of distinctive keypoints, which largely reduces the computational complexity of the state of the art ones, such as DoG, Harris, and Hessian. For the latter, a Multi-task Sparse Representation Classifier (MtSRC) based fine-grained matching strategy is introduced instead of traditional coarse-grained matching, to precisely measure the similarity between the feature sets of the samples. The proposed method is tested on a dataset of 2040 vein images of 204 dorsal hands, and it outperforms the state of the arts clearly proving its effectiveness.
Starting Page	326
Ending Page	333
File Size	2941368
Page Count	8
File Format	PDF
ISBN	9781479978243
DOI	10.1109/ICB.2015.7139057
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-19
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Laplace equations Veins Detectors Feature extraction Probes Image reconstruction
Content Type	Text
Resource Type	Article

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