Efficient iris image segmentation for ATM based approach through fuzzy entropy and graph cut

Abstract

In order to realize accurate personal identification in the ATMS, an efficient iris image segmentation approach based on the fuzzy 4-partition entropy and graph cut is presented which can not only yield noisy segmentation results but short the running time. In this paper, an iterative calculation scheme is presented for reducing redundant computations in fuzzy 4-entropy evaluation. Then the presented algorithm uses the probabilities of 4 fuzzy events to define the costs of 4 label assignments (iris, pupil, background and eyelash) for each region in the graph cut. The final segmentation result is computed using graph cut, which produces smooth segmentation result and yields noise. The experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time. Quantitative evaluations over 20 classic iris images also show that our algorithm outperforms existing iris image segmentation approaches.