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Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks
IEEE Transactions on Industrial Informatics ( IF 12.3 ) Pub Date : 2019-07-01 , DOI: 10.1109/tii.2018.2881343
Sidra Aleem , Bin Sheng , Ping Li , Po Yang , David Dagan Feng

The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multisample dataset. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A Principle Component Analysis-based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multisample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

中文翻译:

快速,准确的视网膜识别系统:使用视网膜血管系统地标

自动化技术的扩展和身份盗用的风险增加,导致人们对自动识别系统的巨大需求成为了重点。由于高识别精度和对人类生理变化的鲁棒性,视网膜生物特征识别系统在该研究领域引起了广泛的关注。在本文中,我们旨在为多样本数据集提供一种自动,快速,准确的视网膜识别系统。所提出的方法使用混合分割技术来分割厚/薄血管,以有效地平衡厚/薄血管之间的小波响应差异。结果,提高了识别精度。提出了一种基于主成分分析的特征处理方法,以有效降低大量血管特征的维数。它大大减少了计算时间,并加快了视网膜识别系统中的匹配过程。该技术在DRIVE,STARE,VARIA,RIDB,HRF,Messildor,DIARETDB0以及作者使用Chen博士(上海交通大学附属第六人民医院)提供的图像创建的每个主题的大型多样本数据库中得到了验证。实验结果表明,所提出的方法优于其他现有技术。在所有这些数据库上,分割均实现了99.65%的总体准确性,识别率达到了99.40%。以及作者使用陈博士(上海交通大学附属第六人民医院)提供的图像创建的每个主题的大型多样本数据库。实验结果表明,所提出的方法优于其他现有技术。在所有这些数据库上,分割均实现了99.65%的总体准确性,识别率达到了99.40%。以及作者使用陈博士(上海交通大学附属第六人民医院)提供的图像创建的每个主题的大型多样本数据库。实验结果表明,所提出的方法优于其他现有技术。在所有这些数据库上,分割均实现了99.65%的总体准确性,识别率达到了99.40%。
更新日期:2019-07-01
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