眼周和虹膜生物识别在ITS认证中的新方法

Manjula Gururaj Rao, Priyanka H, Sumathi Pawar, K. K. Reddy, Usha Divakarla
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引用次数: 0

摘要

在数据技术快速发展的世界中,信息安全是主要的标准。要拥有健壮的系统,安全性是首要任务。个人身份识别系统和生物识别技术在系统安全中起着重要的作用。为了保证系统的安全性,采用了生物识别、OTP生成、虹膜检测等安全措施。生物识别安全系统是一种古老的安全系统。在生物识别安全系统中,主要的挑战是在有限和不受约束的情况下获取视觉波长或近红外照明的图像。提出了一种独特的基于眼周和虹膜传感器融合的身份认证方法。这种方法可用于ITS(智能交通系统)。在智能交通系统中,根据乘客的身份,只有特权或授权的乘客可以乘坐车辆。该系统包括图像预处理后用于分类的特征学习组件。在图像预处理过程中,将眼图像中的切片环状虹膜转化为一个修复区域。利用虹膜定位参数提取局部眼周区域。图像受到各种噪声异常的影响。由于图像的异常,出现了各种数据不足和复杂的情况。为了解决这一问题,一种新的数据增强技术被开发出来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel approach of Using Periocular and Iris Biometric Recognition in the Authentication of ITS
In a fast technology growing world with respect to the data, information security is the major criteria. To have the robust system the security is the foremost task. The personal identification system and biometrics are playing the important part in security of the system. To secure the system, the biometrics, OTP generation, irisdetection etc., are some of the securities concerned aspects are taken. Biometrics security system is one of the old security systems. During the biometric security systems, the main challenge is acquiring images with Visual Wavelength or Near-Infrared lighting in limited and unconstrained situations. This paper proposes a unique approach for personal authentication based on the merging of periocular and iris sensors. This approach may be used in the ITS (Intelligent Transport System). In the ITS, depending on the identification of the passengers, only privileged or the authorized passengers can be transported in the vehicle. The suggested system includes components for feature learning for categorization after image pre-processing. During image pre-processing, a slicedannular iris from an ocular image is turned into a remediedimage region. The local periocular region was extracted using iris localization parameters. Images suffer from varied noise abnormalities. Depending on the abnormality of the image, various data inadequacy and complicating situation arises. To deal with this problem, a novel data augmentation technique has been developed.
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