论文标题
通过对图像特征和机器学习的定量分析进行人类虹膜识别的方法
An approach to human iris recognition using quantitative analysis of image features and machine learning
论文作者
论文摘要
虹膜模式是每个人的独特生物学特征，使其成为人类识别的宝贵而有力的工具。在本文中，通过四个步骤提出了一个有效的虹膜识别框架。 (1) Iris segmentation (using a relative total variation combined with Coarse Iris Localization), (2) feature extraction (using Shape&density, FFT, GLCM, GLDM, and Wavelet), (3) feature reduction (employing Kernel-PCA) and (4) classification (applying multi-layer neural network) to classify 2000 iris images of CASIA-Iris-Interval dataset obtained from 200 volunteers.结果证实，所提出的方案可以提供可靠的预测，准确性高达99.64％。
The Iris pattern is a unique biological feature for each individual, making it a valuable and powerful tool for human identification. In this paper, an efficient framework for iris recognition is proposed in four steps. (1) Iris segmentation (using a relative total variation combined with Coarse Iris Localization), (2) feature extraction (using Shape&density, FFT, GLCM, GLDM, and Wavelet), (3) feature reduction (employing Kernel-PCA) and (4) classification (applying multi-layer neural network) to classify 2000 iris images of CASIA-Iris-Interval dataset obtained from 200 volunteers. The results confirm that the proposed scheme can provide a reliable prediction with an accuracy of up to 99.64%.
