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A pixel-wise, learning-based approach for occlusion estimation of iris images in polar domain
2009
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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On normalized iris images, there are many kinds of noises, such as eyelids, eyelashes, shadows or specular reflections, that often occlude the true iris texture. If high recognition rate is desired, those occluded areas must be estimated accurately in order for them to be excluded during the matching stage. In this paper, we propose a unified, probabilistic and learningbased approach to estimate all kinds of occlusions within one unified model. Experiments have shown that our method not only
doi:10.1109/icassp.2009.4959844
dblp:conf/icassp/LiS09
fatcat:rc2jeriebjbvjbnpc6eg7gyldm