手指宽度选取与识别率对应关系分析

苑玮琦; 朱春艳; 柯丽

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手指宽度选取与识别率对应关系分析

信息科学 | 更新时间：2020-08-12

- 手指宽度选取与识别率对应关系分析
- Analysis of relationship between finger width and recognition rate
- 光学精密工程 2009年17卷第7期页码：1730-1736
- 作者机构：
  
  沈阳工业大学视觉检测技术研究所,辽宁沈阳,110178
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.60672078();No.60472088)()
- DOI：
  中图分类号： TP391.41
- 收稿日期：2008-07-18，
  
  修回日期：2008-09-11，
  
  网络出版日期：2009-07-25，
  
  纸质出版日期：2009-07-25
- 稿件说明：
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苑玮琦, 朱春艳, 柯丽. 手指宽度选取与识别率对应关系分析[J]. 光学精密工程, 2009,17(7): 1730-1736

YUAN Wei-qi, ZHU Chun-yan, KE Li. Analysis of relationship between finger width and recognition rate[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1730-1736
苑玮琦, 朱春艳, 柯丽. 手指宽度选取与识别率对应关系分析[J]. 光学精密工程, 2009,17(7): 1730-1736 DOI：

YUAN Wei-qi, ZHU Chun-yan, KE Li. Analysis of relationship between finger width and recognition rate[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1730-1736 DOI：

摘要

手指宽度是手形识别的有效特征之一

而选取的测量点个数影响识别的准确率和时间。本文研究了选取手指宽度的个数与识别率、匹配时间的对应关系

以用于根据不同的识别率要求估计不同人手选取的宽度个数。实验论证了在定位准确、每个手指选取的宽度个数不少于3个的条件下

识别率可以达到90％以上。提出了自动识别算法

利用曲率法定位特征点

根据特征点计算手指的绝对宽度

应用绝对宽度之间的相对宽度构成特征向量

计算特征向量之间的欧式距离

实现身份识别。实验获得了手指宽度个数与匹配时间的对应关系以及手指宽度个数与识别率的对应关系曲线。结果表明

每个手指选取6个宽度时

就可以获得区分个体的特征量

识别率达到94.03%。

Abstract

The finger width is one of the effective features in the hand shape recognition

and the selected measuring points effect on the accuracy and recognition time.This paper researches the relationship among the selected points

recognition rates and matching time to estimate the selected points in different fingers according to different required recognition rates. Obtained results prove that the recognition rate is more than 90% under the conditions of accurate localization and selected points more than 3 in each finger.An automatic recognition algorithm is proposed

in which the curvature is used to localize feature points

then it used to calculate the absolute widths of fingers.After the eigenvectors are formed by the relative widths between two absolute widths

the Euripides distance between the two eigenvectors is computed to implement the feature recognition. Experiments show that feature values can be acquired to distinguish the human's identity when 6 points of each finger are selected

and the recognition rate can reach 94.03%. Experiment results present the two relationship curves of the number of points

the matching time and the number of points

the recognition rate.

关键词

Keywords

references

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