Iris recognition using curvelet transform and accuracy maximization by particle swarm optimization

A. Ahamed, Syed Irfan Ali Meerza
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Abstract

This study proposes a low complexity iris recognition technique using particle swarm optimization in the curvelet domain transformation. We utilize the standard CASIA-Iris V4 database to test the performance of our proposed method as compared to other state-of-the-art methods. The proposed method provides 99.4% accuracy in recognizing iris images. In addition, our proposed method requires 50% less computational time compared to other state-of-the-art methods.
基于曲线变换和粒子群优化的虹膜识别
提出了一种基于曲线域变换的粒子群优化的低复杂度虹膜识别技术。我们使用标准的CASIA-Iris V4数据库来测试我们提出的方法的性能,并与其他最先进的方法进行比较。该方法对虹膜图像的识别准确率为99.4%。此外,与其他最先进的方法相比,我们提出的方法所需的计算时间减少了50%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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