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Approach has been designed to cope with those kinds of distortions. Extraction of the iris texture information through Haar transformation has the effect of smoothing intra class differences. Matching is performed using local correlation on sub images extracted from the global one. A normalisation is also performed to again favour intra class stability. This way, we expect our system to provide better results in verification mode, when recording conditions are degraded and this exactly what we observe on our experiments realized on the CASIA database after performing some blurring on the original images.
Time does impact the results, however, in no case did the effect of time alone cause overlap between the imposter and authentic distributions. Angle, relative to the acquisition device, introduced greater performance loss compared to time, but angle alone did not lead to an overlap between the imposter and authentic distributions. However, this analysis was limited to a change in pose of 30°. Larger pose changes, and/or improvement of these results, will most likely require an alignment procedure that takes into account the 3-D structure of the iris, in order to better correct for foreshortening and varying illumination across the surface.
This problem is solved by converting the annular iris template into a fixed size. The iris region is transformed from the Cartesian coordinates ( x, y ) to the polar coordinate (r ,θ ) . The rectangular texture size is 64 × 512 in our experiments. 3 Feature Enhancement After normalization, iris templates still have low contrast and non-uniform illumination problems. To eliminate the background brightness, the iris template is divided into non-overlapped 16 × 16 blocks and their means constitute coarse estimates of background illumination for individual blocks.