Abstract
Iris images acquired from a partially cooperating subject often suffer from blur, occlusion due to eyelids, and specular reflections. The performance of existing iris recognition systems degrade significantly on these images. Hence it is essential to select good images from the incoming iris video stream, before they are input to the recognition algorithm. In this paper, we propose a sparsity based algorithm for selection of good iris images and their subsequent recognition. Unlike most existing algorithms for iris image selection, our method can handle segmentation errors and a wider range of acquisition artifacts common in iris image capture. We perform selection and recognition in a single step which is more efficient than devising separate specialized algorithms for the two. Recognition from partially cooperating users is a significant step towards deploying iris systems in a wide variety of applications.
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