Iris Recognition Using the Nonsubsampled Contourlet Transform

Abstract

Biometric information is widely used in user identification system. Because of the unique and invariant properties of the iris through a lifetime, iris recognition is one of the most stable and reliable means in biometric identification. Extracting distinguishable iris features for iris recognition is very important. In this paper, for capturing effective texture features that represent the complex directional structures of an iris image, a new iris recognition method using the nonsubsampled contourlet transform (NSCT) features is proposed. With the shift-invariance, multiscale, and multidirection properties, significant NSCT coefficient features along the radial and angular directions in an iris image can be represented efficiently. Iris segmentation and normalization are considered at first as pre-processing. The modified normalized iris image is obtained from the normalized iris regions for extracting the robust iris features, and then is filtered with the NSCT to obtain the distinct coefficient features in each directional subband. Next, using the NSCT coefficients in each subband, an iris code vector is constructed for iris matching. Comparison of experimental results of the proposed and existing methods with three databases show the effectiveness of the proposed NSCT feature-based iris recognition algorithm, in terms of the three performance measures.