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Multimodal Algorithm for Iris Recognition with Local Topological Descriptors

机译:具有局部拓扑描述符的虹膜识别多峰算法

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This work presents a new method for feature extraction of iris images to improve the identification process. The valuable information of the iris is intrinsically located in its natural texture, and preserving and extracting the most relevant features is of paramount importance. The technique consists in several steps from adquisition up to the person identification. Our contribution consists in a multimodal algorithm where a fragmentation of the normalized iris image is performed and, afterwards, regional statistical descriptors with Self-Organizing-Maps are extracted. By means of a biometric fusion of the resulting descriptors, the features of the iris are compared and classified. The results with the iris data set obtained from the Bath University repository show an excellent accuracy reaching up to 99.867%.
机译:这项工作提出了一种虹膜图像特征提取的新方法,以改进识别过程。虹膜的宝贵信息本质上位于其自然纹理中,因此保留和提取最相关的特征至关重要。该技术包括从获取到人员识别的几个步骤。我们的贡献在于一种多峰算法,该算法对归一化的虹膜图像进行分割,然后提取带有自组织映射的区域统计描述符。通过对生成的描述符进行生物识别融合,可以对虹膜的特征进行比较和分类。从Bath University存储库获得的虹膜数据集的结果显示出极高的准确度,最高可达99.867%。

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