Iris Recognition Using Combination of Dual Tree Rotated Complex Wavelet and Dual Tree Complex Wavelet

Abstract

The increasing requirement of security due to advances in information technologies, especially e-Commerce have led to rapid development of personnel identification /recognition systems based on biometric. A remarkable and important characteristic of the iris is the randomly distributed irregular texture details in all directions. In this paper, the authors have proposed a novel approach of feature extraction of iris image using 2D redundant rotated complex wavelet transform (RCWT) in combination with 2D Dual Trace Complex wavelet Transform(DT-CWT) to obtains the features in 12 different directions as against 3 and 6 directions in Discrete Wavelet Transform (DWT) and Complex Wavelet Transform (CWT) respectively. Iris features are obtained by computing energies and standard deviation of detailed coefficients in 12 directions. The sub-bands f RCWT are derived from sub-bands of CWT by using the suitable mapping rules. Canbera distance is used for matching. The results are obtained using DWT, CWT and combination of CWT and RCWT on UBIRIS database of 2400 images. The performance measure, ZeroFAR is reduced from 6.3 using DWT to 2.9 using the proposed method. The method is also computationally efficient as compared to Gabor Filters.