基于矢量量化的虹膜识别

H. B. Kekre, T. Sarode, V. Bharadi, A. Agrawal, R. J. Arora, M. Nair
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引用次数: 15

摘要

在当今世界,恐怖袭击呈上升趋势,使用可靠的安全系统是必须的。虹膜识别具有通用性、高度唯一性和适度的用户协作性。这使得虹膜识别系统在新兴的安全和认证机制中不可避免。提出了一种基于矢量量化的虹膜识别系统。该系统不需要对虹膜进行预处理和分割。我们测试了LBG, Kekre的比例误差算法(KPE)和Kekre的快速码本生成算法(KFCG)用于聚类目的。结果表明,与LBG和KPE相比,KFCG的计算量减少了99.79%。此外,KFCG方法的准确率为89.10%,优于LBG,准确率约为81.25%。本文对各个方法的性能进行了评价和介绍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Iris Recognition Using Vector Quantization
In today’s world, where terrorist attacks are on the rise, employment of infallible security systems is a must. Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes Iris recognition systems unavoidable in emerging security & authentication mechanisms. We propose an iris recognition system based on vector quantization. The proposed system does not need any pre-processing and segmentation of the iris. We have tested LBG, Kekre’s Proportionate Error Algorithm (KPE) & Kekre’s Fast Codebook Generation Algorithm (KFCG) for the clustering purpose. From the results it is observed that KFCG requires 99.79% less computations as that of LBG and KPE. Further the KFCG method gives best performance with the accuracy of 89.10% outperforming LBG that gives accuracy around 81.25%. Performance of individual methods is evaluated and presented in this paper.
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