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An Efficient Method for Automatic Human Recognition Based on Retinal Vascular Analysis

机译:基于视网膜血管分析的一种有效的人类自动识别方法

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摘要

Biometric security has become more important because of the Increasing activities of hackers and terrorists. Retinal biometric system is one of the most reliable and stable biometrics for the identification/verification of individuals in high security area rather than other biometric. Also no two people have the same retinal pattern and then cannot be stolen or forget. Due to these reasons this study presents a system for individual recognition based on vascular retina pattern. This approach is robust to brightness variations, noise and it is insensitive to rotation. The proposed method consists of three main stages (i.e., preprocessing, feature extraction and finally matching stage). Preprocessing is done to make the required color band separation, remove the rotation appearance which might occur during the scanning process and modify the image brightness to simplify the process of extracting vascular pattern (region of interesting) from input retina (i.e., feature vector) in the feature extraction stage. Finally, the discrimination process of features is evaluated and the results utilized in matching stage. The proposed method is tested on the two publicly available datasets: (ⅰ) DRIVE (Digital Retinal Images for Vessel Extraction) and (ⅱ) STARE (Structured Analysis of the Retina). The achieved accuracy of recognition rate was equal to 100% for all datasets.
机译:由于黑客和恐怖分子的活动日益增多,生物识别安全已变得越来越重要。视网膜生物特征识别系统是用于识别/验证高安全性区域中的个人的最可靠,最稳定的生物特征识别之一,而不是其他生物特征识别。同样,没有两个人具有相同的视网膜模式,因此不能被盗或遗忘。由于这些原因,本研究提出了一种基于血管视网膜图案的个人识别系统。这种方法对于亮度变化,噪声很鲁棒,并且对旋转不敏感。所提出的方法包括三个主要阶段(即,预处理,特征提取和最终匹配阶段)。进行预处理以进行所需的色带分离,消除扫描过程中可能出现的旋转现象,并修改图像亮度,以简化从输入视网膜(即特征向量)提取血管图案(感兴趣区域)的过程。特征提取阶段。最后,对特征的鉴别过程进行了评估,并将结果用于匹配阶段。该方法在两个公开的数据集上进行了测试:(ⅰ)DRIVE(用于血管提取的数字视网膜图像)和(ⅱ)STARE(视网膜的结构分析)。所有数据集的识别率达到的准确度等于100%。

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