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A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Author

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  • Qi Xiong

    (MOE Key Lab for Intelligent Networks and Network Security, School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    International Collage, Hunan University of Arts and Sciences, Changde 415000, China)

  • Xinman Zhang

    (MOE Key Lab for Intelligent Networks and Network Security, School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Shaobo He

    (School of Physics and Electronics, Central South University, Changsha 410083, China)

  • Jun Shen

    (School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia)

Abstract

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

Suggested Citation

  • Qi Xiong & Xinman Zhang & Shaobo He & Jun Shen, 2021. "A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image," Mathematics, MDPI, vol. 9(21), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2790-:d:671445
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    References listed on IDEAS

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    1. Jiamin Wei & YangQuan Chen & Yongguang Yu & Yuquan Chen, 2019. "Optimal Randomness in Swarm-Based Search," Mathematics, MDPI, vol. 7(9), pages 1-19, September.
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    Cited by:

    1. Rui Liu & Yuanbin Mo & Yanyue Lu & Yucheng Lyu & Yuedong Zhang & Haidong Guo, 2022. "Swarm-Intelligence Optimization Method for Dynamic Optimization Problem," Mathematics, MDPI, vol. 10(11), pages 1-28, May.

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