In iris recognition system, accurate iris segmentation and localisation from eye image is the foremost important step. Success rate of any feature extraction algorithm of iris recognition systems is primarily decides by the performance of iris segmentation from an eye image. In the proposed method, the outer boundary of iris is calculated by tracing objects of various shape and structure. For inner iris boundary, two eye images of same subject at different intensities are compared with each other to detect the variation in pupil size. The variation in pupil size is also used for aliveness detection of iris. Thus, this approach is a very promising technique in making iris recognition systems more robust against fake-iris-based spoofing attempts. The algorithm is tested on Phoenix database of 384 images both eyes of 64 subjects. The success rate of accurate iris localisation from eye image is 99.48% with minimal loss of iris texture features in spatial domain as compared to all existing techniques.The processing time required is also comparable with existing techniques.

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In this paper, authors have proposed a novel approach of feature extraction of iris image using multi-directional wavelets obtained by combination of 2D Dual Tree Rotated Complex Wavelet Filters (RCWF) and 2D Dual Trace Complex wavelet Transform(CWT). This method provide 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 coefficient subbands in 12 directions per stage, at 3 level of decomposition. Canbera distance is used for matching. The results are obtained using DWT, CWT combination of CWT and RCWF on UBIRIS database of 24000 image. The performance measure, ZeroFAR, is reduced from 6.3 using DWT to 2.6 using proposed method. The results are also comparable with the Daughman method. The method is also computationally efficient as compared to Gabor Filters.

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