基于广义高斯分布FDCT_Wrap和FSVM的虹膜识别

何振红

doi:10.11959/j.issn.1000-0801.2016200

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基于广义高斯分布FDCT_Wrap和FSVM的虹膜识别

研究与开发 | 更新时间：2024-06-05

- 基于广义高斯分布FDCT_Wrap和FSVM的虹膜识别
- Iris recognition based on generalized Gaussian distribution FDCT_Wrap and FSVM
- 电信科学 2016年32卷第7期页码：126-131
- 作者机构：
- 作者简介：
  
  [ "何振红（1979-），女，甘肃民族师范学院讲师，主要研究方向为图像处理、模式识别。" ]
- 基金信息：
  
  甘肃省高等学校科研项目;Scientific Research Project in Gansu Provincial Colleges and Universities(2015B-119)
- DOI：10.11959/j.issn.1000-0801.2016200
  中图分类号： TP391
- 网络出版日期：2016-07，
  
  纸质出版日期：2016-07-15
- 稿件说明：
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何振红. 基于广义高斯分布FDCT_Wrap和FSVM的虹膜识别[J]. 电信科学, 2016,32(7):126-131.

Zhenhong HE. Iris recognition based on generalized Gaussian distribution FDCT_Wrap and FSVM[J]. Telecommunications science, 2016, 32(7): 126-131.
何振红. 基于广义高斯分布FDCT_Wrap和FSVM的虹膜识别[J]. 电信科学, 2016,32(7):126-131. DOI： 10.11959/j.issn.1000-0801.2016200.

Zhenhong HE. Iris recognition based on generalized Gaussian distribution FDCT_Wrap and FSVM[J]. Telecommunications science, 2016, 32(7): 126-131. DOI： 10.11959/j.issn.1000-0801.2016200.

摘要

为了提高虹膜识别的准确率，提出了一种改进曲波变换的虹膜识别算法。首先对预处理后的虹膜图像进行Wrapping算法的快速离散曲波变换，提取不同尺度和不同方向的曲波子带系数矩阵的均值、方差和能量，然后利用广义高斯分布估算各子带的权值，为分类能力较强的特征向量赋予较大权值，构成虹膜图像的特征向量。最后采用模糊支持向量机和二叉决策树相结合的分类器进行匹配识别。采用UBIRIS和CASIA虹膜数据库对算法性能进行测试。实验结果表明，该算法能更好地提高虹膜识别准确率和效率，具有可行性。

Abstract

In order to improve the accuracy rate of iris recognition

an improved curvelet transform algorithm for iris recognition was proposed.Firstly

the iris image was decomposed with fast discrete curvelet transform by wrapping algorithm.Mean variance and energy of curvelet sub-band coefficients in different scales and different orientations were extracted.The weights of sub-bands were estimated by generalized Gaussian distribution.The feature vectors with stronger classification ability had large weight

which were calculated to constitute feature vectors of iris image.Finally

feature vectors were matched and recognized by classifier combined with fuzzy support vector machine and binary decision tree.The algorithm performances were tested with UBIRIS and CASIA iris database.Simulation results show that the proposed algorithm has higher recognition accuracy rate and efficiency.It is feasibility.

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references

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