An Efficient Iris Recognition System Based on Intersecting Cortical Model Neural Network
An Efficient Iris Recognition System Based on Intersecting Cortical Model Neural Network
复制标题
基于相交皮层模型神经网络的高效虹膜识别系统
DOI：
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发表时间：
2008
影响因子：
0.9
通讯作者：
Yide Ma
中科院分区：
文献类型：
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作者：
Guangzhu Xu;Zaifeng Zang;Yide Ma
虹膜识别已被证明是非常准确的人类身份。本文提出了一种基于交叉皮层模型（ICM）神经网络的虹膜识别系统。第一部分是图像预处理，分为三个步骤。首先，基于局部区域实现虹膜定位。然后将定位的虹膜区域归一化为具有固定大小的矩形区域。最后实现了虹膜图像的增强。第二部分利用ICM神经网络生成虹膜编码，并计算两个虹膜编码之间的汉明距离来衡量它们之间的差异。为了评估所提出的算法的性能，使用CASIA v1.0虹膜图像数据库和识别结果是令人鼓舞的。
Iris recognition has been shown to be very accurate for human identification. In this article, an efficient iris recognition system based on Intersecting Cortical Model (ICM) neural network is presented which includes two parts mainly. The first part is image preprocessing which has three steps. First, iris location is implemented based on local areas. Then the localized iris area is normalized into rectangular region with a fixed size. At last the iris image enhancement is implemented. In the second part, the ICM neural network is used to generate iris codes and the Hamming Distance between two iris codes is calculated to measure the dissimilarity of them. In order to evaluate the performance of the proposed algorithm, CASIA v1.0 iris image database is used and the recognition results are encouraging.