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

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...

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Autores principales:	Qi Xiong, Xinman Zhang, Shaobo He, Jun Shen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	image enhancement fractional-order chaos swarm intelligence sparrow search algorithm contrast limited adaptive histogram equalization (CLAHE) iris images Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/0947ef01b2864665b83bd12988fbdec8
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spelling	oai:doaj.org-article:0947ef01b2864665b83bd12988fbdec82021-11-11T18:19:39ZA Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image10.3390/math92127902227-7390https://doaj.org/article/0947ef01b2864665b83bd12988fbdec82021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2790https://doaj.org/toc/2227-7390At 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.Qi XiongXinman ZhangShaobo HeJun ShenMDPI AGarticleimage enhancementfractional-order chaosswarm intelligencesparrow search algorithmcontrast limited adaptive histogram equalization (CLAHE)iris imagesMathematicsQA1-939ENMathematics, Vol 9, Iss 2790, p 2790 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	image enhancement fractional-order chaos swarm intelligence sparrow search algorithm contrast limited adaptive histogram equalization (CLAHE) iris images Mathematics QA1-939
spellingShingle	image enhancement fractional-order chaos swarm intelligence sparrow search algorithm contrast limited adaptive histogram equalization (CLAHE) iris images Mathematics QA1-939 Qi Xiong Xinman Zhang Shaobo He Jun Shen A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image
description	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.
format	article
author	Qi Xiong Xinman Zhang Shaobo He Jun Shen
author_facet	Qi Xiong Xinman Zhang Shaobo He Jun Shen
author_sort	Qi Xiong
title	A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image
title_short	A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image
title_full	A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image
title_fullStr	A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image
title_full_unstemmed	A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image
title_sort	fractional-order chaotic sparrow search algorithm for enhancement of long distance iris image
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0947ef01b2864665b83bd12988fbdec8
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A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

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