This paper studies the iris recognition problem in the degraded iris images captured in non-ideal imaging conditions. In these circumstances iris recognition becomes challenging because of noisy factors such as the off-axis imaging, pose variation, image blurring, illumination change, occlusion, specular highlights and noise. The noisy iris images increase the intra-individual variations, thus markedly degrading recognition accuracy. To overcome these problems, we propose a new iris recognition algorithm for noisy iris images. First, we use a classification method which discriminates the ‘‘left or right eye’’ on the basis of the eyelash distribution and SR points. Since the iris pattern of the left eye differs from that of the right eye, the 1st step classification can enhance the accuracy of iris recognition. Second we normalize the irises to the same size without rubber sheet model. Our new method notably prevents iris texture deformation especially those images captured from long distances. Third, the separability between intra- and inter-classes is increased by using the 2nd step classification based on the ‘‘color information’’ of the iris region. They are measured by using the Euclidean distance calculated with the color space models such YCbCr, HSV, and lab. Finally, ‘‘textural information’’ of the iris region is used for classification. we apply 1-D Log Gabor filter on LTP map of iris region of gray scale images to afford sets of iris codes from iris textures. Experiments were conducted on the NICE.II training dataset selected from UBIRIS.v2 database. The results showed that the proposed method performed better than existing methods and showed that the decidability value was 1.9294.

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