A novel retinal identification system
A novel retinal identification system
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DOI：
10.1155/2008/280635
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发表时间：
2008-01-01
影响因子：
1.9
通讯作者：
Moin, Mohammad-Shahram
中科院分区：
文献类型：
--
作者：
Farzin, Hadi;Abrishami-Moghaddam, Hamid;Moin, Mohammad-Shahram
本文提出了一种新的生物特征识别系统，具有高性能的基础上获得的特征，从人的视网膜图像。该系统由血管分割、特征生成和特征匹配三个主要模块组成。血管分割模块的作用是从视网膜图像中提取血管模式。特征生成模块包括以下几个阶段。首先，找到光盘，并在分割图像中选择其周围的圆形感兴趣区域（ROI）。然后，使用极坐标变换，从每个ROI创建旋转不变模板。在下一阶段中，使用小波变换在三个不同尺度上分析这些模板，以根据其直径大小分离血管。在最后一个阶段中，使用每个尺度中的血管位置和方向来定义数据库中每个受试者的特征向量。对于特征匹配，我们引入了一种改进的相关性度量来获得特征向量的每个尺度的相似性指数。然后，我们计算的总价值的相似性指数通过求和尺度加权相似性指数。在一个包含60个受试者的300张视网膜图像的数据库上的实验结果表明，我们的识别系统的平均等错误率为1%。
This paper presents a novel biometric identification system with high performance based on the features obtained from human retinal images. This system is composed of three principal modules including blood vessel segmentation, feature generation, and feature matching. Blood vessel segmentation module has the role of extracting blood vessels pattern from retinal images. Feature generation module includes the following stages. First, the optical disk is found and a circular region of interest (ROI) around it is selected in the segmented image. Then, using a polar transformation, a rotation invariant template is created from each ROI. In the next stage, these templates are analyzed in three different scales using wavelet transform to separate vessels according to their diameter sizes. In the last stage, vessels position and orientation in each scale are used to define a feature vector for each subject in the database. For feature matching, we introduce a modified correlation measure to obtain a similarity index for each scale of the feature vector. Then, we compute the total value of the similarity index by summing scale-weighted similarity indices. Experimental results on a database, including 300 retinal images obtained from 60 subjects, demonstrated an average equal error rate equal to 1 percent for our identification system.