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| Finger Vein Recognition Based on Curve Descriptor |
| Su Dan1,2, Wang Xinqiang1, Liu Yuhang2, Lu Yaopeng2, Li Ting1,2, Nie Zedong2#* |
1(School of Electronic Engineering and Automation,Guilin University of Electronic Technology, Guilin 541004,China)
2(Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,Guangdong, China) |
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Abstract Finger vein recognition has become a research hotspot in the field of identity recognition due to its advantages of high anti-counterfeiting, uniqueness, stability, and liveness detection. At present, most recognition algorithms based on vein structure features consider the feature of detail points, but it is easy to ignore the curve feature of the vein network structure, which would cause the loss of part of structural information and affect the recognition results. To solve the problems, this paper proposed a finger vein recognition algorithm based on curve descriptor. Firstly, the skeleton structure of finger veins was extracted, the intersection points and endpoints of the veins were detected, and the intersection points and endpoints were used to divide the vein skeleton into several curve segments. Secondly, the curve arc descriptors and intersecting arc descriptor were proposed based on the relative position and shape characteristics of the intersection points and curve segments, and the structural feature matrix of finger veins was extracted. Finally, the matching intersecting arc pair was calculated according to the weighted distance formula proposed, and then the degree of image matching was judged. Experiments were carried out on a finger vein database with a sample size of 840 images. The experimental results showed that the equal error rate of the LBP, LTP and SURF algorithms was 4.47%, 3.99% and 6.08% respectively, while the equal error rate of the proposed method was only 1.63% that was lower than that of LBP, LTP and SURF algorithms, indicating that the method in this paper had a certain universality and application prospect in finger vein recognition.
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Received: 13 August 2021
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Corresponding Authors:
*E-mail: zd.nie@siat.ac.cn
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