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.
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Acknowledgements
This work is supported by the Major Project of National Natural Science Foundation (No. 91218301), the Fundamental Research Funds for Central Universities-Innovation Team Project (No. JBK150503) and the Central Universities-Yong Scholar Development Project (No. JBK150134), the Youth Fund Project of National Natural Science Foundation (No. 61502396). Besides that, this work is also supported by the Internet Financial Innovation and Regulatory Collaborative Innovation Center.
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Yin, S., Wang, Y., Wang, T. (2016). Efficient Iris Image Segmentation for ATM Based Approach Through Fuzzy Entropy and Graph Cut. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_20
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DOI: https://doi.org/10.1007/978-3-319-42996-0_20
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