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Iris Recognition based on Directional filtering and Classifier fusion
Noncooperative environment increases the probability of capturing heterogeneous images (regarding focus, contrast, or brightness) with several noise factors such as iris obstructions and reflections. This paper focuses a novel iris recognition algorithm, suitable for such uncontrolled environment. The proposed method divides the normalized iris image into eight regions avoids the noise, localized in some of the iris subparts corrupting the whole iris features, reduces the effect of noise due to noncooperative behavior in an authentication system. Rotation invariant features are obtained from the decomposed directional subbands coefficients of the normalized iris image block using optimal projection analysis. A fusion algorithm combines the matching scores from individual subimages based on the quality measure, improves the performance. Experimental result shows a substantial decrease of the false rejection rates in the recognition of noisy iris images.
Biometrics, iris recognition, optimal projection analysis Quality measure and undecimated pyramid.
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