Fingerprint Matching Based on Composite Gradient Vector

YUAN Heng; WANG Zhi-hong; JIANG Wen-tao

doi:10.3969/j.issn.0372-2112.2017.04.021

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Fingerprint Matching Based on Composite Gradient Vector

更新时间：2025-07-16

- Fingerprint Matching Based on Composite Gradient Vector
- Acta Electronica Sinica Vol. 45, Issue 4, Pages: 912-921(2017)
- 作者机构：
  
  1. 辽宁工程技术大学工商管理学院,辽宁,葫芦岛,125105
  2. 辽宁工程技术大学软件学院,辽宁,葫芦岛,125105
  3. 辽宁工程技术大学工商管理学院,辽宁,葫芦岛,125105
  4. 辽宁工程技术大学软件学院,辽宁,葫芦岛,125105
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61172144)
- DOI：10.3969/j.issn.0372-2112.2017.04.021
  CLC： TP391.41
- Published Online：25 April 2017，
  
  Published：2017
- 稿件说明：
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YUAN Heng, WANG Zhi-hong, JIANG Wen-tao. Fingerprint Matching Based on Composite Gradient Vector[J]. Acta Electronica Sinica, 2017, 45(4): 912-921.
DOI：

YUAN Heng, WANG Zhi-hong, JIANG Wen-tao. Fingerprint Matching Based on Composite Gradient Vector[J]. Acta Electronica Sinica, 2017, 45(4): 912-921. DOI： 10.3969/j.issn.0372-2112.2017.04.021.

摘要

针对接触式指纹识别中存在的非线性形变问题，提出了一种基于复合梯度向量（Composite Gradient Vector，CGV）的指纹匹配算法.该算法首先在经过预处理的指纹图像上建立直角坐标系和第一个基向量，并对指纹图像进行旋转；然后在旋转后的指纹图像上采样基向量，组建向量簇，提取极大梯度向量，并以极大梯度向量为元素组建复合梯度向量；最后以分层标记规则对复合梯度向量进行分层标记，通过对指纹库进行指纹检索、复合梯度向量匹配、维度和梯度匹配，识别出指纹图像.实验结果表明，该方法克服了接触式指纹识别中旋转、偏移、拉伸等问题，有较强的抗非线性形变能力，同时具有较快的识别速度和较高的识别准确率.

Abstract

An approach based on composite gradient vector is proposed to overcome the problem of nonlinear distortion in contact fingerprint identification.Firstly

rectangular coordinate system and the first base vector are set up

and then the fingerprint image is rotated.Base vectors are collected on rotated fingerprint image.All the base vectors are set into vector cluster.Then the basis vectors are made into greatly gradient vector.Finally

hierarchical markup method is used for markup on the composite gradient vector.The target fingerprint image is recognized by fingerprint searching

compound gradient vector matching

dimension and gradient matching auxiliary.Experiment results show that the proposed approach has overcome the problem of rotation

translation and scaling situation and has a strong ability of anti-nonlinear deformation with fast recognition speed and high recognition accuracy.

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