Shift Invariant Iris Feature Extraction Using Rotated Complex Wavelet and Complex Wavelet for Iris Recognition System

R. Bodade, S. Talbar
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引用次数: 23

Abstract

In this paper, authors have proposed a novel approach of  feature extraction of iris images using combination of  2D Dual Tree Rotated Complex Wavelet Transform (RCWT) and 2D Dual Trace Complex Wavelet Transform(CWT). This method provides features in 12 directions against 3 and 6 directions in DWT and CWT respectively. Iris features are obtained by computing  energies and standard deviation of detailed coefficients in 12 directions per stage, at 3 levels of decomposition. Canberra distance is used for matching. The results are obtained using DWT, CWT combination of CWT and RCWT on UBIRIS database of 2400 images. The performance measure, ZeroFAR, is reduced from 6.3 using DWT to 2.7 using proposed method. The method is also computationally efficient as compared to Gabor Filters.
旋转复小波和复小波在虹膜识别系统中的移位不变特征提取
本文提出了一种结合二维双树旋转复小波变换(RCWT)和二维双迹复小波变换(CWT)的虹膜图像特征提取方法。该方法分别对DWT和CWT中的3个方向和6个方向提供12个方向的特征。虹膜特征是通过计算每阶段12个方向详细系数的能量和标准差,分3个层次分解得到的。堪培拉距离用于匹配。在UBIRIS数据库的2400幅图像上,分别采用小波变换、小波变换和小波变换相结合的方法得到结果。使用所提出的方法,性能指标ZeroFAR从使用DWT的6.3降低到2.7。与Gabor滤波器相比,该方法的计算效率也很高。
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