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New biometric approach based on geometrical humain brain patterns recognition: Some preliminary results

机译:基于几何疏肝脑模式识别的新型生物识别方法:一些初步结果

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In this paper, we describe a new biometric approach based on geometrical characteristics of brain shape. Specifically, we use these geometrics characteristics as a biometric feature to identify individuals. For this purpose, Magnetic Resonance Imaging (MRI) images are considered. We show that using a single slice from an MRI volumetric image, acquired at a given level, one can extract many significant geometrical descriptors related to inter-individual variability of brain shape that can be also used to identify individuals. Explicitly, the proposed biometric approach combines two main phases. In the first phase, features extraction (FE) are achieved in order to obtain brain geometrical descriptors vector, called in this work GDB vector. A second phase is called similarity measurement (SM). Finally, the proposed algorithm is evaluated on the Open Access Series of Imaging Studies (OASIS) database containing brain MRI Images. Results using 220 classes show that high accuracy of 98.76% to identify individuals are obtained.
机译:在本文中,我们描述了一种基于脑形状的几何特征的新型生物识别方法。具体地,我们使用这些几何特征作为生物识别功能来识别个体。为此目的,考虑磁共振成像(MRI)图像。我们示出了使用从给定水平获取的MRI容量图像的单个切片,可以提取与脑形状的间间可变性相关的许多有关的几何描述符,这些符也可以用于识别个体。明确地,所提出的生物识别方法结合了两个主要阶段。在第一阶段中,为了获得脑几何描述符向量来实现特征提取(FE),以便在这项工作GDB载体中被称为脑。第二阶段称为相似性测量(SM)。最后,在含有脑MRI图像的成像研究(OASIS)数据库的开放访问系列中评估所提出的算法。使用220级的结果显示,获得了98.76%的高精度才能识别个体。

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