An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm
This study aims to enhance the localization of the inner and outer circles of the iris while addressing issues of excessive invalid computations and inaccuracies. To achieve this objective, diverse methods are employed to improve the process to varying extents. Initially, the image undergoes pre-pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/21/4454 |
| _version_ | 1797632013314818048 |
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| author | Shanwei Niu Zhigang Nie Jiayu Liu Mingcao Chu |
| author_facet | Shanwei Niu Zhigang Nie Jiayu Liu Mingcao Chu |
| author_sort | Shanwei Niu |
| collection | DOAJ |
| description | This study aims to enhance the localization of the inner and outer circles of the iris while addressing issues of excessive invalid computations and inaccuracies. To achieve this objective, diverse methods are employed to improve the process to varying extents. Initially, the image undergoes pre-processing operations, including grayscale conversion, mathematical morphological transformation, noise reduction, and image enhancement. Subsequently, the accurate localization of the inner and outer edges is achieved by applying algorithms such as Canny edge detection and the Hough transform, allowing for the determination of their corresponding center and radius values within the iris image. Lastly, an improvement is made to the particle swarm optimization algorithm by combining various algorithms, namely LinWPSO, RandWPSO, contraction factor, LnCPSO, and AsyLnCPSO, employing mechanisms such as simulated annealing and the ant colony algorithm. Through dual validation on the CASIA-Iris-Syn dataset and a self-built CASIA dataset, this approach significantly enhances the precision of iris localization and reduces the required iteration count. |
| first_indexed | 2024-03-11T11:31:50Z |
| format | Article |
| id | doaj.art-957b65508789475190bf32d9a7ad2c98 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T11:31:50Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-957b65508789475190bf32d9a7ad2c982023-11-10T15:01:30ZengMDPI AGElectronics2079-92922023-10-011221445410.3390/electronics12214454An Application Study of Improved Iris Image Localization Based on an Evolutionary AlgorithmShanwei Niu0Zhigang Nie1Jiayu Liu2Mingcao Chu3College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaIntelligent Manufacturing and Control Engineering College, Shandong University of Petrochemical Technology, Dongying 257000, ChinaThis study aims to enhance the localization of the inner and outer circles of the iris while addressing issues of excessive invalid computations and inaccuracies. To achieve this objective, diverse methods are employed to improve the process to varying extents. Initially, the image undergoes pre-processing operations, including grayscale conversion, mathematical morphological transformation, noise reduction, and image enhancement. Subsequently, the accurate localization of the inner and outer edges is achieved by applying algorithms such as Canny edge detection and the Hough transform, allowing for the determination of their corresponding center and radius values within the iris image. Lastly, an improvement is made to the particle swarm optimization algorithm by combining various algorithms, namely LinWPSO, RandWPSO, contraction factor, LnCPSO, and AsyLnCPSO, employing mechanisms such as simulated annealing and the ant colony algorithm. Through dual validation on the CASIA-Iris-Syn dataset and a self-built CASIA dataset, this approach significantly enhances the precision of iris localization and reduces the required iteration count.https://www.mdpi.com/2079-9292/12/21/4454image processingiris localizationHough transformparticle swarm algorithmsimulated annealingant colony algorithm |
| spellingShingle | Shanwei Niu Zhigang Nie Jiayu Liu Mingcao Chu An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm Electronics image processing iris localization Hough transform particle swarm algorithm simulated annealing ant colony algorithm |
| title | An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm |
| title_full | An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm |
| title_fullStr | An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm |
| title_full_unstemmed | An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm |
| title_short | An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm |
| title_sort | application study of improved iris image localization based on an evolutionary algorithm |
| topic | image processing iris localization Hough transform particle swarm algorithm simulated annealing ant colony algorithm |
| url | https://www.mdpi.com/2079-9292/12/21/4454 |
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