Optimizing Fused Face-Iris Biometric Recognition Accuracy and Timing Using Improved Mayfly Algorithm

Adegbola Isaac Oladimeji, Ayisat Wuraola Asaju-Gbolagade    Kazeem Alagbe Gbolagade

Abstract

A multimodal biometrics system is presented in order to improve the recognition performance, system complexity, security, and applicability of current biometrics applications. In this study, an improved Mayfly optimization algorithm was used as a feature selection method to improve recognition accuracy and timing for a fused face-iris biometric recognition system. The improved Mayfly algorithm is an enhancement to the original Mayfly optimization algorithm. The Mayfly algorithm is an optimization method based on the behavior of mayflies that provides a powerful hybrid algorithm structure. It combines the best features of particle swarm optimization, genetic algorithms, and the firefly algorithm. Simulation experiments demonstrated that it is capable of optimizing both benchmark functions, but with significant limitations. Due to the random selection procedure used, which allows the existing algorithm to exploit specific areas in the search space, notable shortcomings included slow convergent rate, premature convergent, and potential imbalance between exploration and exploitation. As a result, the Mayfly algorithm has found it difficult to solve high-dimensional problem spaces such as feature selection. The Mayfly algorithm is enhanced in this study with the roulette wheel selection method, which replaces the random selection method used in the existing Mayfly algorithm. Both the existing Mayfly algorithm and the newly developed improved Mayfly algorithm were used as feature selection on a fused face-iris recognition system in order to improve recognition accuracy and time complexity. The results of simulation experiments revealed that the Improved Mayfly algorithm increased the recognition accuracy and time complexity of the fused face and iris biometrics recognition system.

Keywords: Fused face iris, Roulette Wheel Selection, Random Selection, Mayfly Optimization Algorithm

How to cite this article: Adegbola Isaac Oladimeji, Ayisat Wuraola Asaju-Gbolagade    Kazeem Alagbe GbolagadeOptimizing Fused Face-Iris Biometric Recognition Accuracy and Timing Using Improved Mayfly AlgorithmJournal of Materials Engineering, Structures and Computation 1(1) 2022 pp. 9-20

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