Low complexity iris recognition using curvelet transform

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 significantly reduced. The zero-crossings of the transform coefficients are used to generate the iris codes. Since only the coefficients from approximation subbands are used, it reduces the length of the code. The iris codes are matched employing the correlation coefficient. Extensive experiments are carried out using a number of standard databases such as CASIA- V3, UBIRIS.v1 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 significant reduction in the computational time, as compared to those of the state-of-the-art techniques.