Interval Type-2 Fuzzy Integral Based Iris Recognition

Thiyam Churjit Meetei, Shahin Ara Begum

Abstract


Most of iris recognition system uses single matcher or classifier for decision making. In this paper, an Interval Type-2 Fuzzy Integral (IT2 FI) is proposed as a new approach to combine the match scores of three classifiers viz. fuzzy k-NN and two backpropagation neural networks with logsig and tansig transfer functions in order to improve the performance as well as robustness of the system. A comparison with other fusion rules viz. the sum rule, max-rule, product-rule and fuzzy integral, is also conducted. From the experimental results, it is observed that the proposed Interval Type-2 Fuzzy Integral based matching score fusion approach outperforms some of the existing fusion methods.

Keywords


Iris recognition; Match score fusion; Fusion rules; Fuzzy integral; Interval type-2

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References


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DOI: https://doi.org/10.26483/ijarcs.v8i5.3255

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