Interval Type-2 Fuzzy Integral Based Iris Recognition

Thiyam Churjit Meetei, Shahin Ara Begum


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.


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

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