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Iris localization using rough entropy and CSA: A soft computing approach
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DOI：10.1016/j.asoc.2018.02.047.png)
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Abstract
En 中文
Identification of a person depends on the proper extraction of the iris region. Segmentation, being the first step in iris analysis, constitutes the most important phase in iris localization. Images are often captured in non-ideal conditions, and are incomplete with different kinds of associated uncertainties. Therefore, iris segmentation assumes paramount importance towards its subsequent localization and analysis. A novel soft-computing approach is proposed for the segmentation of iris based on rough entropy, with localization using circular sector analysis (CSA); thereby minimizing uncertainties. We compare the performance of this algorithm with that by the circular Hough transform, which is state-of-the-art in approximating the iris region although being computationally intensive. The proposed rough entropy based segmentation, followed by CSA for localization of iris, is found to perform more efficiently and accurately in comparison to the state-of-the-art methodologies. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Rough sets
Iris segmentation
Circular sector analysis (CSA)
Rough entropy
Uncertainty modeling
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