[EEE/OSA/[APR Interational Conference on [nfonnatics, Electronics & Vision Low Complexity Iris Recognition using Curvelet Transform Afsana Ahamed Department of EEE Bangladesh University of Engineering and Technology Dhaka, Bangladesh afsana4@gmail.com Abstract-In this paper, a low complexity technique is proposed for iris recognition in the curvelet transform domain. The proposed method does not require the detection of outer boundary and decreases unwanted artefacts such as the eyelid and eyelash. Thus, the time required for preprocessing of an iris image is signifcantly reduced. The zero-crossings of the transform coefcients are used to generate the iris codes. Since only the coefcients from approximation subbands are used, it reduces the length of the code. The iris codes are matched employing the correlation coefcient. Extensive experiments are carried out using a number of standard databases such as CASIA- V3, UBIRIS.vl and UPOL. The results reveal that the proposed method using the curvelet transform provides a very high degree of accuracy (about 100%) over a wide range of images with a low equal error rate (EER) and a signifcant reduction in the computational time, as compared to those of the state-of-the-art techniques. Keywords-iris recognition; curvelet transform; correct recognition rate; equal error rate 1. I NTRODUCTION Biometric recognition systems are becoming popular in recent times due to their robustness to exteral effects and for the ability to offer a high degree of uniqueness and stability for person identifcation [I]. A biometric recognition system uses a person's physiological and/or behavioral characteristics as features that include face, fngerprint, palm print, voice patters, ear, iris and gait. Recently, iris recognition has emerged as a superior method of identifcation technology. The human iris is an annular region between the pupil (generally darkest portion of the eye) and sclera. [t has many interlacing minute characteristics such as feckles, coronas, stripes, frrows, crypts and so on. The iris patters of the two eyes of an individual or those of identical twins are completely independent and uncorrelated, which is largely detennined by the pre-natal development. [t is reported that an iris being similar to another oane is of extremely low probability, about 1 in 10 72 [2]. [n addition, the iris is visible, making it highly suitable for practical noninvasive identifcation processes. The concept of automated lflS recognition is frst introduced by Flom and Safr in [2]. Since then, a number of methods have been proposed in the literature for effective iris 978-1-4673-1154-0112/$3l.00 ©2012 IEEE Mohammed Imamul Hassan Bhuiyan Associate Professor, Department of EEE Bangladesh University of Engineering and Technology Dhaka, Bangladesh imamhas@gmail.com recogmtlOn. [n the pioneering work of Daugman [3,4], multiscale Gabor flters are used to demodulate texture phase structure information of the iris. This results in 1024 complex-valued phasors at different scales. Each phasor is quantized to one of the four quadrants in the complex plane. The resulting 2048-component iris code is used to describe an iris. The difference between a pair of iris codes is measured by their Hamming distance. The method of Daugman provides high accuracy and is employed in several commercial iris recognition systems. The drawback of this method is its rather long length of the iris code considering the large volume of iris data to be compared. Wildes [5] represents the iris texture with a Laplacian pyramid constructed with four different resolution levels and uses the normalized corelation to determine whether the input image and the model image are fom the same class. However, it gives lower accuracy as compared to that of [3] and suffers fom a high computational complexity. Boles and Boashash [6] calculate the zero-crossings of an one-dimensional (1 D) wavelet transform at various resolution levels of a concentric circle on an iris image to generate the iris code. [n [7], the 4 th level Haar wavelet coefcients are quantized to form the iris code, later classifed using a competitive learing neural network. [n [8], overlapped patches of normalized iris are subjected to discrete cosine transform (DCT); the differences of the coefcients are then quantized to fonn the iris code, later matched by Hamming distance. In [9], phase components of 2-D Fourier transform coefcients are used as features that are classifed using the Hamming distance. In [10], both the phase and magnitude of the Gabor wavelet outputs are used as features that are matched region-wise using the Euclidean distance. [n [11], the iris features are extracted using the oriented separable wavelet transforms, called directionlets, and compared using a weighted Hamming distance. In [12], a co-occurrence matrix is generated using the values of various statistical measures calculated fom the Contourlet coefcient for matching. In [13], a novel normalization method is employed for iris localization and the corresponding Contourlet coefcients are classifed using a Support Vector Machine (SVM). Note that the methods of [12] and [13] report recognition rates for a rather limited number of classes. Recently, the multi-scale Curvelet transform [14] has emerged as a highly effective tool for sparse image representation, much better than the traditional wavelets. However, to the best of our knowledge very limited work has been done on curvelet transform-based iris recognition ICIEV 2012 MATLAB code can be download from matlab1.com matlab1.com