离焦状态下的模糊掌纹识别

林森; 苑玮琦

doi:10.3788/OPE.20132103.0734

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离焦状态下的模糊掌纹识别

信息科学 | 更新时间：2020-08-12

- 离焦状态下的模糊掌纹识别
- Blurred palmprint recognition under defocus status
- 光学精密工程 2013年21卷第3期页码：734-741
- 作者机构：
  
  沈阳工业大学视觉检测技术研究所2. 沈阳工业大学信息科学与工程学院
- 作者简介：
- 基金信息：
  
  高等学校博士学科点专项科研基金(20136101110019)
- DOI：10.3788/OPE.20132103.0734
  中图分类号： TP391.4
- 收稿日期：2012-10-24，
  
  修回日期：2012-12-12，
  
  网络出版日期：2013-03-20，
  
  纸质出版日期：2013-03-15
- 稿件说明：
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林森苑玮琦. 离焦状态下的模糊掌纹识别[J]. 光学精密工程, 2013,21(3): 734-741

LIN Sen YUAN Wei-qi. Blurred palmprint recognition under defocus status[J]. Editorial Office of Optics and Precision Engineering, 2013,21(3): 734-741
林森苑玮琦. 离焦状态下的模糊掌纹识别[J]. 光学精密工程, 2013,21(3): 734-741 DOI： 10.3788/OPE.20132103.0734.

LIN Sen YUAN Wei-qi. Blurred palmprint recognition under defocus status[J]. Editorial Office of Optics and Precision Engineering, 2013,21(3): 734-741 DOI： 10.3788/OPE.20132103.0734.

摘要

针对掌纹识别时非接触信号采集可能出现的离焦状态会导致掌纹模糊，从而降低识别系统性能的问题，提出了一种基于稳定特征的模糊掌纹识别方法。建立了掌纹的离焦退化数学模型；在分析模糊机理的基础上，使用拉普拉斯平滑变换提取模糊掌纹的低频系数作为稳定特征，利用特征向量之间的欧式距离进行匹配和判别。文中给出了算法的步骤，并通过实验确定了需要选取的低频系数的个数。在建立的SUT-D模糊掌纹库上进行了识别测试，结果表明本文算法的等误率可达17.101 7%，与传统的DCT变换及Eigen Palm，Palm Code等8种典型识别方法比较，等误率最高可降低7.908 4%。这些结果显示本文方法不但能够提升识别效果，而且特征维数较低，改善了模糊掌纹识别系统的性能。

Abstract

As the defocus status in non-contact signal acquisition for palmprint recognition might blur palmprint and degrade the performance of a recognition system

a novel scheme based on stable features was proposed for the blurred palmprint recognition. Firstly

a mathematical model of defocus degeneration was established. Then

the blur mechanism was analyzed in detail and the Laplacian Smoothing Transform (LST) was employed to extract low-frequency coefficients from blurred palmprint as stable features. Furthermore

the Euclidean distance between the feature vectors was used for matching and discriminating. With the experiments

the operation steps of the algorithm was given and the number of low-frequency coefficients were determined. The experiments based on the self-made SUT-D blurred palmprint database were performed. Obtained results show that the proposed algorithm can get Equal Error Rate (EER) of 17.1017%

which has been maximally reduced by 7.9084% compared with those from other typical recognition methods such as traditional Discrete Cosine Transform (DCT)

Eigen Palm and the Palm Code. These results show that the proposed scheme not only has higher recognition efficiency but also has a low dimension

so it significantly improves the performance of the blurred palmprint recognition systems.

关键词

Keywords

references

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