Finger vein recognition algorithm based on FAST feature extraction

Wei-jian LI; Jian JING; Si DI

doi:10.3788/OPE.20202802.0507

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Finger vein recognition algorithm based on FAST feature extraction

Information Sciences | 更新时间：2020-07-16

- Finger vein recognition algorithm based on FAST feature extraction
- Optics and Precision Engineering Vol. 28, Issue 2, Pages: 507-514(2020)
- 作者机构：
  
  1.广州中国科学院先进技术研究所, 广东广州 511458
  2.成都理工大学信息科学与技术学院, 四川成都 610059
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20202802.0507
  CLC： TP391.41
- Received：02 August 2019，
  
  Revised：11 October 2019，
  
  Accepted：11 October 2019，
  
  Published：25 February 2020
- 稿件说明：
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Wei-jian LI, Jian JING, Si DI. Finger vein recognition algorithm based on FAST feature extraction[J]. Optics and precision engineering, 2020, 28(2): 507-514.
DOI：

Wei-jian LI, Jian JING, Si DI. Finger vein recognition algorithm based on FAST feature extraction[J]. Optics and precision engineering, 2020, 28(2): 507-514. DOI： 10.3788/OPE.20202802.0507.

摘要

现有的指静脉识别方法通常以包含静脉分布的灰度图为对象进行算法设计。但由于采集装置的局限性

光照强度的不确定性

手指血管周围组织的复杂性等

原始图像即使经过图像预处理过程

得到的灰度图中依然会存在不规则的阴影和非静脉特征

这可能使得被提取出的静脉特征不具有很好的代表性和区分性，从而降低识别结果的准确性。因此

本文提出以包含指静脉分布的二值图为对象进行算法设计，从而在识别过程中尽可能减少非静脉因素的干扰。在特征提取上采用了适于二值图特征点检测的加速分割测试特征提取算法，提取出静脉纹理边缘中符合要求的像素点作为特征点，进而对检测到的特征点进行向量化描述。同时为了提高匹配的精度

提出了基于圆形邻域的匹配算法

使用加权匹配距离描述图像之间的相似程度。采用山东大学公开的手指静脉数据库进行算法性能测试，平均识别率为0.993，等误率为0.0196。上述结果证明了算法的有效性，为指静脉识别算法设计提供了新的思路。

Abstract

Finger vein recognition algorithms are usually based on grayscale images of vein distributions. However

because of the limitations of image acquisition devices

the uncertainties in the illumination intensity and the complexity of the tissue around the finger vessels (among other factors)even after image processing cause the resulting grayscale images to possess irregular shadows and non-venous characteristics

which may reduce the accuracy of the recognition results. Therefore

this paper proposed a new finger vein identification algorithm based on binary images to minimize the interference of non-venous factors in the identification process. First

the features from accelerated segment test algorithm was used to extract the pixel points at the edges of vein textures as feature points

and then the feature vectors were constructed. Further

to improve the matching precision

a new matching algorithm based on circular neighborhoods was proposed. The weighted matching distance was used to describe the degree of similarity between images. The average recognition rate of the proposed method when applied to the finger vein database published by Shandong University is 0.993

and the equal error rate is 0.019 6.These results demonstrate the effectiveness of the algorithm and provide a new basis for the design of vein recognition algorithms.

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references

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